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Lee, Si-Ho, Bong-Ju Kim, and Seon-Bong Lee. "Study on Image Correction and Optimization of Mounting Positions of Dual Cameras for Vehicle Test." Energies 14, no. 16 (August 9, 2021): 4857. http://dx.doi.org/10.3390/en14164857.
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Among surrounding information-gathering devices, cameras are the most accessible and widely used in autonomous vehicles. In particular, stereo cameras are employed in academic as well as practical applications. In this study, commonly used webcams are mounted on a vehicle in a dual-camera configuration and used to perform lane detection based on image correction. The height, baseline, and angle were considered as variables for optimizing the mounting positions of the cameras. Then, a theoretical equation was proposed for the measurement of the distance to the object, and it was validated via vehicle tests. The optimal height, baseline, and angle of the mounting position of the dual camera configuration were identified to be 40 cm, 30 cm, and 12°, respectively. These values were utilized to compare the performances of vehicles in stationary and driving states on straight and curved roads, as obtained by vehicle tests and theoretical calculations. The comparison revealed the maximum error rates in the stationary and driving states on a straight road to be 3.54% and 5.35%, respectively, and those on a curved road to be 9.13% and 9.40%, respectively. It was determined that the proposed method is reliable because the error rates were less than 10%.
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Grabowski, Dariusz, and Andrzej Czyżewski. "System for monitoring road slippery based on CCTV cameras and convolutional neural networks." Journal of Intelligent Information Systems 55, no. 3 (September 9, 2020): 521–34. http://dx.doi.org/10.1007/s10844-020-00618-5.
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Abstract The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transfer-learning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.
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Ali, S. Y., O. Al-Saleh, and P. A. Koushki. "Effectiveness of Automated Speed-Monitoring Cameras in Kuwait." Transportation Research Record: Journal of the Transportation Research Board 1595, no. 1 (January 1997): 20–26. http://dx.doi.org/10.3141/1595-04.
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In 1994 the General Traffic Department installed automatic radar cameras to monitor traffic speed at a number of strategic roadway locations in Kuwait. The aim was to lower the number of high-speed violations and thus reduce road accidents. Recent traffic safety records point to an increase in both the number of violations and the occurrence of road accidents. It is argued in this paper that without live enforcement support and active follow-up of camera-recorded violations, the effectiveness of these cameras in improving road safety is insignificant at best, particularly in the undisciplined driving environment of the oil-rich nations in the Middle East. The speed of traffic was simultaneously measured via radar instruments both at the automatic camera site and at sections approximately 1 km before or after or before and after the cameras at eight camera locations. Measurements were recorded for six 1/2-hr periods at each site for a total of 72 hr over a period of 3 months, so that morning, afternoon, and after-dark hours, as well as different days of the week and roadway types, were covered. Analysis of the speed data showed that for the three daily periods and various roadway types, traffic speeds were consistently higher in sections before or after or before and after the automatic camera at the camera site. Statistical tests indicated that the difference in speed measured at and away from the cameras was at the 99 percent level. The findings demonstrate that in a traffic environment characterized by poor driving behavior, inconsistent and piecemeal driver education programs, and insufficient presence of law enforcement officials, reliance on automatic cameras alone to reduce traffic violations is doomed to fail.
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Kuehnle, Andreas, and Wilco Burghout. "Winter Road Condition Recognition Using Video Image Classification." Transportation Research Record: Journal of the Transportation Research Board 1627, no. 1 (January 1998): 29–33. http://dx.doi.org/10.3141/1627-05.
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Sweden spends 1.7 billion Crowns on winter road maintenance annually. A large part of this money goes into plowing, salting, and sanding of the roads. The decision about what maintenance to perform is made, in part, based on data received from road weather information stations, some of which are also equipped with video cameras. These video cameras form an additional unexploited sensor for determining the road condition during winter. Images taken from a handheld roadside video camera are investigated here to see if it is possible to determine the road state (dry, wet, snowy, icy, snowy with tracks) from the video images alone. The system is intended to supplement the other weather station measurements, such as temperature and wind speed, and make better maintenance decisions and quality control of maintenance possible. The results indicate that it is possible to distinguish between all road states except for ice/wet and ice/tracks. Typical class separations are a Mahanalobis distance between 0 and 2. Neural networks with three or four input features, three to five hidden neurons, and a sigmoid-sigmoid-linear architecture are used to classify the road state. Rates of correct classification are typically 40 to 50 percent with these networks. There are useful feature combinations, including purely monochrome features, which do not depend on the network architecture.
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Zhang, Haojie, David Hernandez, Zhibao Su, and Bo Su. "A Low Cost Vision-Based Road-Following System for Mobile Robots." Applied Sciences 8, no. 9 (September 13, 2018): 1635. http://dx.doi.org/10.3390/app8091635.
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Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.
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Zhu, Zijian, Xu Li, Jianhua Xu, Jianhua Yuan, and Ju Tao. "Unstructured Road Segmentation Based on Road Boundary Enhancement Point-Cylinder Network Using LiDAR Sensor." Remote Sensing 13, no. 3 (January 30, 2021): 495. http://dx.doi.org/10.3390/rs13030495.
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The segmentation of unstructured roads, a key technology in self-driving technology, remains a challenging problem. At present, most unstructured road segmentation algorithms are based on cameras or use LiDAR for projection, which has considerable limitations that the camera will fail at night, and the projection method will lose one-dimensional information. Therefore, this paper proposes a road boundary enhancement Point-Cylinder Network, called BE-PCFCN, which uses Point-Cylinder in order to extract point cloud features directly and integrates the road enhancement module to achieve accurate unstructured road segmentation. Firstly, we use the improved RANSAC-Boundary algorithm to calculate the rough road boundary point set, training in the same parameters with the original point cloud as a submodule. The whole network adopts the encoder and decoder structure, using Point-Cylinder as the basic module, while considering the data locality and the algorithm complexity. Subsequently, we made an unstructured road data set for training and compared it with existing LiDAR(Light Detection And Ranging) semantic segmentation algorithms. Finally, the experiment verified the robustness of BE-PCFCN. The road intersection-over-union (IoU) was increased by 4% when compared with the best existing algorithm, reaching 95.6%. Even on unstructured roads with an extremely irregular shape, BE-PCFCN also currently has the best segmentation results.
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Rhanizar, Asmae, and Zineb El Akkaoui. "A Predictive Framework of Speed Camera Locations for Road Safety." Computer and Information Science 12, no. 3 (July 30, 2019): 92. http://dx.doi.org/10.5539/cis.v12n3p92.
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Road traffic crashes are a public health issue due to their terrible impact on individuals, communities, and countries. Studies affirmed that vehicle speed is a major contributor to crash likelihood and severity. At the same time, they identified Automated Speed Enforcement (ASE) systems, namely speed cameras, as a highly effective measure to reduce excessive and inappropriate speed, and thus improving road safety. However, identifying optimum sites for fixed speed camera placement stays an open issue in the literature, although it is a key factor that guarantees the efficiency of such ASE systems. This paper describes a predictive framework of speed camera locations using a classification algorithm that can predict, for each section of a given road network, its pertinence as a speed camera location. First, we identify a set of features as predictors of the classification algorithm, that we have argued their goodness through correlation tests. Second, for training our algorithm, data from road controlled sections, corresponding to existing speed cameras, is exploited. Each section class reflects the contribution level of the ASE system (good, neutral, or bad) to road safety. Third, as a proofof-concept, the framework has been implemented and deployed on the Moroccan road network. The results showed that Random Forest classifier is the best performing model attaining an accuracy of 95% and a precision of 88%. Further, a tool was developed to visualize updated classification results on a Moroccan road network map to support authorities in their decision making process.
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Liu, Chun Feng, Shan Shan Kong, and Hai Ming Wu. "Research on a Single Camera Location Model and its Application." Applied Mechanics and Materials 50-51 (February 2011): 468–72. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.468.
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Digital cameras have been widely used in the areas of road transportation, railway transportation as well as security system. To address the position of digital camera in these fields this paper proposed a geometry calibration method based on feature point extraction of arbitrary target. Under the meaning of the questions, this paper first defines four kinds of coordinate system, that is the world coordinate system. The camera's optical center of the coordinate system is the camera coordinate system, using the same point in different coordinate system of the coordinate transformation to determine the relationship between world coordinate system and camera coordinate. And thus determine the camera's internal parameters and external parameters, available transformation matrix and translation vector indicated by the camera's internal parameters of the external parameters and the establishment of a single camera location model. According to the model, using the camera's external parameters to be on the target circle center point in the image plane coordinates.
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Coronell, Garis, Julián Arellana, and Víctor Cantillo. "LOCATION OF SPEED CONTROL CAMERAS ON HIGHWAYS: A GEOSPATIAL ANALYSIS." Transport 36, no. 3 (August 17, 2021): 199–212. http://dx.doi.org/10.3846/transport.2021.15117.
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This research proposes a methodology to identify critical sections of highways where the location of speeding control may be beneficial. The method relies on a spatial and statistical analysis of infrastructure risks, along with traffic accident frequency and severity. A relevant feature of this methodology is related to its potential to be used in areas where there are no detailed historical records about traffic crashes, which is common in Global South countries. We applied the methodology to a rural road network in Colombia, where a recent law established that technical criteria should support the location of speed cameras. The case study uses accident information from six years, and risk data from a road safety audit carried out in the area under study. Even though historical records of accidents in the area were not fully available, the methodology allowed prioritising speed camera installations in the zone and identifying the relevant variables to define camera location. The relevant variables were the geometric characteristics of the road, traffic flows, risk factors, and proximity to populated centres. The use of speed controls should be part of a road safety management system, which allows defining camera location according to robust technical criteria.
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Koukoumidis, Emmanouil, Margaret Martonosi, and Li-Shiuan Peh. "Leveraging Smartphone Cameras for Collaborative Road Advisories." IEEE Transactions on Mobile Computing 11, no. 5 (May 2012): 707–23. http://dx.doi.org/10.1109/tmc.2011.275.
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Graham, Daniel J., Cian Naik, Emma J. McCoy, and Haojie Li. "Do speed cameras reduce road traffic collisions?" PLOS ONE 14, no. 9 (September 16, 2019): e0221267. http://dx.doi.org/10.1371/journal.pone.0221267.
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Taylor, Brendan D., and Ross L. Goldingay. "Squirrel gliders use roadside glide poles to cross a road gap." Australian Mammalogy 35, no. 1 (2013): 119. http://dx.doi.org/10.1071/am12013.
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Tall wooden poles placed along roadsides may enable road crossing by gliding mammals. We investigated the use of 12-m-high roadside glide poles at one location in Brisbane. Camera traps on roadside poles detected squirrel gliders (Petaurus norfolcensis) on one or both poles on 60 of 310 operational nights including at least 16 confirmed crossings during 125 nights when both roadside cameras operated concurrently. This provides definitive evidence that squirrel gliders will readily use tall wooden poles to make glide-crossings of two-lane roads. This is consistent with other investigations of glide poles and suggests that poles should be installed more broadly to reconnect habitat for gliding mammals.
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Dahal, Ashok, Eric Golab, Rajender Garlapati, Varun Ravi Kumar, and Senthil Yogamani. "RoadEdgeNet: Road Edge Detection System Using Surround View Camera Images." Electronic Imaging 2021, no. 17 (January 18, 2021): 210–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.17.avm-210.
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Road Edge is defined as the borderline where there is a change from the road surface to the non-road surface. Most of the currently existing solutions for Road Edge Detection use only a single front camera to capture the input image; hence, the system’s performance and robustness suffer. Our efficient CNN trained on a very diverse dataset yields more than 98% semantic segmentation for the road surface, which is then used to obtain road edge segments for individual camera images. Afterward, the multi-cameras raw road edges are transformed into world coordinates, and RANSAC curve fitting is used to get the final road edges on both sides of the vehicle for driving assistance. The process of road edge extraction is also very computationally efficient as we can use the same generic road segmentation output, which is computed along with other semantic segmentation for driving assistance and autonomous driving. RoadEdgeNet algorithm is designed for automated driving in series production, and we discuss the various challenges and limitations of the current algorithm.
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Frentzos, E., E. Tournas, and D. Skarlatos. "DEVELOPING AN IMAGE BASED LOW-COST MOBILE MAPPING SYSTEM FOR GIS DATA ACQUISITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 235–42. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-235-2020.
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Abstract. The aim of this study is to develop a low-cost mobile mapping system (MMS) with the integration of vehicle-based navigation data and stereo images acquired along vehicle paths. The system consists of a dual frequency GNSS board combined with a low-cost INS unit and two machine vision cameras that collect colour image data for road and roadside objects. The navigation data and the image acquisition are properly synchronized to associate position and attitude to each digital frame captured. In this way, upon pixel location of objects appearing on the video frames, their absolute geographical coordinates can be extracted by employing standard photogrammetric methods. Several calibration steps are implemented before survey operation: camera calibration, relative orientation between cameras and determination of rotation angles and offsets between vehicle and cameras reference frames. A software tool has been developed to facilitate and speed up the calibration procedures. Furthermore, easy object coordinate extraction is supported, either in auto mode, where the conjugate image coordinates are obtained in real time using image correlation techniques. Several surveying experiments were executed to certify and check the accuracy and efficiency of the system. From the achieved results, the developed system is efficient for collecting and positioning road spatial objects such as such as road boundaries, traffic lights, road signs, power poles, etc, more rapidly and less expensively. The obtained absolute positional accuracy is less than 1 meter, depending on the availability and quality of the GPS signal.
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Sahin, Furkan. "Long-Range, High-Resolution Camera Optical Design for Assisted and Autonomous Driving." Photonics 6, no. 2 (June 25, 2019): 73. http://dx.doi.org/10.3390/photonics6020073.
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High-quality cameras are fundamental sensors in assisted and autonomous driving. In particular, long-range forward-facing cameras can provide vital information about the road ahead, including detection and recognition of objects and early hazard warning. These automotive cameras should provide high-resolution images consistently under extreme operating conditions of the car for robust operation. This paper aims to introduce the design of fixed-focus, passively athermalized lenses for next-generation automotive cameras. After introducing an overview of essential and desirable features of automotive cameras and state-of-the-art, based on these features, two different camera designs that can achieve traffic sign recognition at 200 m distance are presented. These lenses are designed from scratch, with a unique design approach that starts with a graphical lens material selection tool and arrives at an optimized design with optical design software. Optical system analyses are performed to evaluate the lens designs. The lenses are shown to accomplish high contrast from − 40 °C to 100 °C and allow for a 4 × increase in resolution of automotive cameras.
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Ogawa, Keiichi, Hiroshi Tsukaguchi, Yoongho Ahn, and Makoto Kawai. "Effective Planning of Road Monitoring Systems for Cultural Heritage Disaster Mitigation." Journal of Disaster Research 6, no. 1 (February 1, 2011): 132–41. http://dx.doi.org/10.20965/jdr.2011.p0132.
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Road monitoring systems can be effective in the mitigation of damage to cultural heritage sites when disaster strikes historical cities. In this research, the effects of road monitoring systems for cultural heritage disaster mitigation are estimated. The effective location planning of the surveillance cameras is considered based on the locations of cultural heritage sites and the characteristics of the city’s road network. To select the best locations, the genetic algorithm is applied. It is demonstrated that use of the genetic algorithm to select the location of the road monitoring system’s surveillance cameras greatly increases the system’s effectiveness.
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Read, J. L., A. J. Bengsen, P. D. Meek, and K. E. Moseby. "How to snap your cat: optimum lures and their placement for attracting mammalian predators in arid Australia." Wildlife Research 42, no. 1 (2015): 1. http://dx.doi.org/10.1071/wr14193.
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Context Automatically activated cameras (camera traps) and automated poison-delivery devices are increasingly being used to monitor and manage predators such as felids and canids. Maximising visitation rates to sentry positions enhances the efficacy of feral-predator management, especially for feral cats, which are typically less attracted to food-based lures than canids. Aims The influence of camera-trap placement and lures were investigated to determine optimal monitoring and control strategies for feral cats and other predators in two regions of semi-arid South Australia. Methods We compared autumn and winter capture rates, activity patterns and behaviours of cats, foxes and dingoes at different landscape elements and with different lures in three independent 6 km × 3 km grids of 18 camera-trap sites. Key results Neither visual, olfactory or audio lures increased recorded visitation rates by any predators, although an audio and a scent-based lure both elicited behavioural responses in predators. Cameras set on roads yielded an eight times greater capture rate for dingoes than did off-road cameras. Roads and resource points also yielded highest captures of cats and foxes. All predators were less nocturnal in winter than in autumn and fox detections at the Immarna site peaked in months when dingo and cat activity were lowest. Conclusions Monitoring and management programs for cats and other predators in arid Australia should focus on roads and resource points where predator activity is highest. Olfactory and auditory lures can elicit behavioural responses that render cats more susceptible to passive monitoring and control techniques. Dingo activity appeared to be inversely related to fox but not cat activity during our monitoring period. Implications Optimised management of feral cats in the Australian arid zone would benefit from site- and season-specific lure trials.
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Darmawan, Matthew Rio, Heru Purnomo Ipung, and Maulahikmah Galinium. "Experiment of Multispectral Sensing Sensor for Urban Road Materials in Outdoor Environment." ICONIET PROCEEDING 2, no. 3 (February 13, 2019): 191–99. http://dx.doi.org/10.33555/iconiet.v2i3.32.
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This research is the first attempt to conduct several experiments of multispectralsensing sensor for urban road materials in outdoor environment. This research aims to classifyfive urban road materials that are aggregates, asphalts, concrete, clay, natural fibre includingvegetation and water. There were 9 cameras in the multispectral sensing sensor. Seven cameraattached with narrow band optical filter with the centre spectrum at 710nm, 730nm, 750nm,800nm, 870nm, 905nm and 950nm. One camera attached with 720 nm normalization band useshigh pass optical filter. Another camera attached with UV/IR cut optical filter works as a RGBcamera. The images results, that have been taken, are processed in MATLAB to get the imagingindex results from the multispectral system. Naïve Bayes classifier is used in Weka to classifythe urban road materials with vegetation and water. The first classification and testing thatclassifies five urban road materials with vegetation and water have accuracy results ranged from0 % to 32% while the accuracy results without vegetation and water have better accuracy resultsranged from 0 % to 55 %.
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Colley, Michael, Stephen C. Lougheed, Kenton Otterbein, and Jacqueline D. Litzgus. "Mitigation reduces road mortality of a threatened rattlesnake." Wildlife Research 44, no. 1 (2017): 48. http://dx.doi.org/10.1071/wr16130.
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Context Reducing road mortality is essential to reptile conservation in regions with dense road networks. The Georgian Bay, Ontario population of the eastern massasauga rattlesnake (Sistrurus catenatus) is designated as Threatened, in part because of high road mortality. In Killbear Provincial Park, four ecopassages and barrier fencing were constructed along three busy park roads to reduce road mortality of massasaugas. Aim Although mitigation of road mortality has been widely recommended and in some instances implemented for reptiles, effectiveness of mitigation efforts is often inadequately evaluated. The goals of our study were to use long-term data to quantify the effectiveness of ecopassages and barrier fencing in reducing massasauga fatalities on roads, and to evaluate the potential of these structures to serve as movement corridors for individual snakes. Methods We used five approaches to assess the overall efficacy of mitigation efforts: (1) comparison of pre- and post-mitigation road mortality; (2) camera traps in ecopassages to document massasauga and predator presence; (3) automated tag readers in ecopassage entrances to detect PIT-tagged individuals; (4) an experiment to assess massasauga willingness to enter and travel through ecopassages; and (5) measurement of temperature fluctuations in ecopassages to assess thermal suitability for massasaugas. Key results We found a significant decrease in road mortality of massasaugas on stretches of park roads associated with ecopassages and barrier fencing post construction. Automated tag readers and cameras detected the presence of massasaugas and other animals within the ecopassages, and experimental data showed that massasaugas willingly entered, and in some cases crossed through, ecopassages. Conclusion Our evaluation of mitigation structures determined that they successfully reduce road mortality and provide potential movement corridors between bisected habitats, provided that intense maintenance of the fencing is conducted yearly. We also demonstrated the need to utilise a combination of multiple post-monitoring methods to effectively evaluate mitigation structures. Implications This study provides a template for construction of similar mitigation in other key locations where reptile road mortality occurs.
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Brunken, Hauke, and Clemens Gühmann. "Road Surface Reconstruction by Stereo Vision." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88, no. 6 (November 18, 2020): 433–48. http://dx.doi.org/10.1007/s41064-020-00130-z.
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AbstractThis paper covers the problem of road surface reconstruction by stereo vision with cameras placed behind the windshield of a moving vehicle. An algorithm was developed that employs a plane-sweep approach and uses semi-global matching for optimization. Different similarity measures were evaluated for the task of matching pixels, namely mutual information, background subtraction by bilateral filtering, and Census. The chosen sweeping direction is the plane normal of the mean road surface. Since the cameras’ position in relation to the base plane is continuously changing due to the suspension of the vehicle, the search for the base plane was integrated into the stereo algorithm. Experiments were conducted for different types of pavement and different lighting conditions. Results are presented for the target application of road surface reconstruction, and they show high correspondence to laser scan reference measurements. The method handles motion blur well, and elevation maps are reconstructed on a millimeter-scale, while images are captured at driving speed.
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Choi, Kyoungtaek, Ho Jung, and Jae Suhr. "Automatic Calibration of an Around View Monitor System Exploiting Lane Markings." Sensors 18, no. 9 (September 5, 2018): 2956. http://dx.doi.org/10.3390/s18092956.
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This paper proposes a method that automatically calibrates four cameras of an around view monitor (AVM) system in a natural driving situation. The proposed method estimates orientation angles of four cameras composing the AVM system, and assumes that their locations and intrinsic parameters are known in advance. This method utilizes lane markings because they exist in almost all on-road situations and appear across images of adjacent cameras. It starts by detecting lane markings from images captured by four cameras of the AVM system in a cost-effective manner. False lane markings are rejected by analyzing the statistical properties of the detected lane markings. Once the correct lane markings are sufficiently gathered, this method first calibrates the front and rear cameras, and then calibrates the left and right cameras with the help of the calibration results of the front and rear cameras. This two-step approach is essential because side cameras cannot be fully calibrated by themselves, due to insufficient lane marking information. After this initial calibration, this method collects corresponding lane markings appearing across images of adjacent cameras and simultaneously refines the initial calibration results of four cameras to obtain seamless AVM images. In the case of a long image sequence, this method conducts the camera calibration multiple times, and then selects the medoid as the final result to reduce computational resources and dependency on a specific place. In the experiment, the proposed method was quantitatively and qualitatively evaluated in various real driving situations and showed promising results.
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Almoshaogeh, Meshal, Radfan Abdulrehman, Husnain Haider, Fawaz Alharbi, Arshad Jamal, Saif Alarifi, and Md Shafiquzzaman. "Traffic Accident Risk Assessment Framework for Qassim, Saudi Arabia: Evaluating the Impact of Speed Cameras." Applied Sciences 11, no. 15 (July 21, 2021): 6682. http://dx.doi.org/10.3390/app11156682.
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Saudi Arabia is one of the countries with the highest number of road accidents and associated fatalities in the world. Speeding has been identified as an important cause of increased traffic accidents, which also aggravate their severity. Road safety improvement strategies are primarily based on the accurate identification of accident hotspots. Installing speed cameras at a network level is an expensive road safety measure, and its spatiotemporal effectiveness should be assessed. In this study, a traffic accident risk assessment framework has been developed and implemented on the 84 km long Buraydah Ring Road in the Qassim region of Saudi Arabia. The selected highway was divided into 42 (×2 km long) segments using the ArcGIS software. A risk scoring scheme was developed to incorporate both the frequency and severity of road accidents. Speed cameras installation at various segments showed a 70% decline in total accident counts, 53% in accidents with property damage, 84% decline in accidents causing injury, and complete absence of accidents with fatalities. The 48% segments were identified as hotspots with risk level ≥ medium, while the speed cameras installation completely eliminated the hotspots from the study area. The proposed framework can be implemented on major high-speed highways, accommodating high traffic volumes, for hotspot identification and evaluation of various road safety measures in Saudi Arabia and elsewhere.
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Li, Haojie, and Daniel J. Graham. "Heterogeneous treatment effects of speed cameras on road safety." Accident Analysis & Prevention 97 (December 2016): 153–61. http://dx.doi.org/10.1016/j.aap.2016.09.007.
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Hanel, A., L. Hoegner, and U. Stilla. "TOWARDS THE INFLUENCE OF A CAR WINDSHIELD ON DEPTH CALCULATION WITH A STEREO CAMERA SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 461–68. http://dx.doi.org/10.5194/isprsarchives-xli-b5-461-2016.
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Stereo camera systems in cars are often used to estimate the distance of other road users from the car. This information is important to improve road safety. Such camera systems are typically mounted behind the windshield of the car. In this contribution, the influence of the windshield on the estimated distance values is analyzed. An offline stereo camera calibration is performed with a moving planar calibration target. In a standard procedure bundle adjustment, the relative orientation of the cameras is estimated. The calibration is performed for the identical stereo camera system with and without a windshield in between. The base lengths are derived from the relative orientation in both cases and are compared. Distance values are calculated and analyzed. It can be shown, that the difference of the base length values in the two cases is highly significant. Resulting effects on the distance calculation up to a half meter occur.
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Hanel, A., L. Hoegner, and U. Stilla. "TOWARDS THE INFLUENCE OF A CAR WINDSHIELD ON DEPTH CALCULATION WITH A STEREO CAMERA SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 461–68. http://dx.doi.org/10.5194/isprs-archives-xli-b5-461-2016.
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Stereo camera systems in cars are often used to estimate the distance of other road users from the car. This information is important to improve road safety. Such camera systems are typically mounted behind the windshield of the car. In this contribution, the influence of the windshield on the estimated distance values is analyzed. An offline stereo camera calibration is performed with a moving planar calibration target. In a standard procedure bundle adjustment, the relative orientation of the cameras is estimated. The calibration is performed for the identical stereo camera system with and without a windshield in between. The base lengths are derived from the relative orientation in both cases and are compared. Distance values are calculated and analyzed. It can be shown, that the difference of the base length values in the two cases is highly significant. Resulting effects on the distance calculation up to a half meter occur.
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Sirirattanapol, Chairath, Masahiko NAGAI, Apichon Witayangkurn, Surachet Pravinvongvuth, and Mongkol Ekpanyapong. "Bangkok CCTV Image through a Road Environment Extraction System Using Multi-Label Convolutional Neural Network Classification." ISPRS International Journal of Geo-Information 8, no. 3 (March 4, 2019): 128. http://dx.doi.org/10.3390/ijgi8030128.
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Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been deployed recently in various places for surveillance and traffic monitoring. Instead of deploying new sensors designed specifically for monitoring road conditions, images and location information from existing cameras can be used to obtain precise environmental information. Therefore, we propose a road environment extraction framework that covers different situations, such as raining and non-raining scenes, daylight and night-time scenes, crowded and non-crowded traffic, and wet and dry roads. The framework is based on CCTV images from a Bangkok metropolitan dataset, provided by the Bangkok Metropolitan Administration. To obtain information from CCTV image sequences, multi-label classification was considered by applying a convolutional neural network. We also compared various models, including transfer learning techniques, and developed new models in order to obtain optimum results in terms of performance and efficiency. By adding dropout and batch normalization techniques, our model could acceptably perform classification with only a few convolutional layers. Our evaluation showed a Hamming loss and exact match ratio of 0.039 and 0.84, respectively. Finally, a road environment monitoring system was implemented to test the proposed framework.
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Sonnleitner, Erik, Oliver Barth, Alexander Palmanshofer, and Marc Kurz. "Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data." Applied Sciences 10, no. 18 (September 10, 2020): 6270. http://dx.doi.org/10.3390/app10186270.
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Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.
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Gonzalez, Ramon, Francisco Rodriguez, Jose Luis Guzman, Cedric Pradalier, and Roland Siegwart. "Combined visual odometry and visual compass for off-road mobile robots localization." Robotica 30, no. 6 (October 5, 2011): 865–78. http://dx.doi.org/10.1017/s026357471100110x.
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SUMMARYIn this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.
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Wu, Yuezhou, Changjiang Liu, Shiyong Lan, and Menglong Yang. "3D Road Scene Monitoring Based on Real-Time Panorama." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/403126.
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Road monitoring helps to control the regional traffic situation so as to adjust the traffic flow. Real-time panorama is conducive to timely treat traffic accidents and to greatly improve traffic capacity. This paper designs a 3D road scene monitoring framework based on real-time panorama. The system is the combination of large scale panorama, satellite map textures, and 3D scene model, in which users can ramble freely. This paper has the following contributions. Firstly, land-points were extracted followed by motion detection, then comotion algorithm was applied to land-points from adjacent cameras, and homography matrix was constructed. Secondly, reference camera was chosen and transformed to overhead viewpoint; subsequently multiviews were morphed to the same viewpoint and stitched to panorama. Finally, the registration based on high-precision GPS information between 2D road panorama and 3D scene model was also proposed. The proposed framework has been successfully applied to a large road intersection monitoring. Experimental results are furnished at the end of the paper.
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Julge, Kalev, Artu Ellmann, and Romet Köök. "Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks." Baltic Journal of Road and Bridge Engineering 14, no. 1 (March 28, 2019): 1–17. http://dx.doi.org/10.7250/bjrbe.2019-14.430.
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Unmanned aerial vehicle photogrammetry is a surveying technique that enables generating point clouds, 3D surface models and orthophoto mosaics. These are based on photos captured with a camera placed on an unmanned aerial vehicle. Within the framework of this research, unmanned aerial vehicle photogrammetry surveys were carried out over a sand and gravel embankment with the aim of assessing the vertical accuracy of the derived surface models. Flight altitudes, ground control points and cameras were varied, and the impact of various factors on the results was monitored. In addition, the traditional real-time-kinematic Global Navigation Satellite System surveys were conducted for verifications. Surface models acquired by different methods were used to calculate volumes and compare the results with requirements set by Estonian Road Administration. It was found that with proper measuring techniques an accuracy of 5.7 cm for the heights were achieved.
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Zou, Haochen, Keyan Cao, and Chong Jiang. "Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data." ISPRS International Journal of Geo-Information 10, no. 3 (March 17, 2021): 177. http://dx.doi.org/10.3390/ijgi10030177.
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Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.
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Липунов, K. Lipunov, Лемешева, E. Lemesheva, Тихомиров, and P. Tikhomirov. "INCREASE OF PROPUSKNOY SPOSOBNOSTI ROUTE M-3 IN THE BRYANSK REGION." Alternative energy sources in the transport-technological complex: problems and prospects of rational use of 2, no. 2 (December 17, 2015): 657–60. http://dx.doi.org/10.12737/19488.
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In this paper, an analysis of the road network and infrastructure of the Bryansk region on the example of the federal highway M-3 "Ukraine" (Kiev highway). The description of the state of road fabric, its length and the number of lanes. Transport-performance of the road network and bridges do not meet the stresses of modern heavy vehicles and traffic. The planned activities of the area is not considered raising technical category road. To improve the region´s economy and the country´s need to carry out the improvement of road infrastructure, taking into account the different methods that will reduce accidents on the roads, increase and accelerate the flow of goods through the region. It proposed a number of measures to enhance the capacity: to improve the quality of road surface with the transfer of the 1st category; adding overlapping areas; the creation of parking areas and service centers for freight transport, and installation of video cameras for control and monitoring of the road.
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Brunken, Hauke, and Clemens Gühmann. "Pavement distress detection by stereo vision / Straßenzustandserkennung durch stereoskopische Bildverarbeitung." tm - Technisches Messen 86, s1 (September 1, 2019): 42–46. http://dx.doi.org/10.1515/teme-2019-0046.
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AbstractFor road maintenance up-to-date information about road conditions is important. Such information is currently expensive to obtain. Specially equipped measuring vehicles have to perform surface scans of the road, and it is unclear how to automatically Ąnd faulty sections in these scans. This research solves the problem by stereo vision with cameras mounted behind the windshield of a moving vehicle so that the system can easily be integrated into a large number of vehicles. The stereo images are processed into a depth map of the road surface. In a second step, color images from the cameras are combined with the depth map and are classified by a convolutional neural network. It is shown that the developed system is able to Ąnd defects that require knowledge about surface deformations. These defects could not have been found on monocular images. The images are taken at usual driving speed.
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BASKOV, V. N., and E. I. ISAEVA. "ESTIMATION OF THE EFFICIENCY OF ITS ELEMENTS APPLICATIONIN THE CITY OF SARATOV." World of transport and technological machines 73, no. 2 (2021): 63–71. http://dx.doi.org/10.33979/2073-7432-2021-73-2-63-71.
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An analysis of the accident rate in the city of Saratov, places of concentration of road traffic accidents showed the need to install photo and video cameras. The assessment of the most emergency areas in Saratov and the experience of installing photo and video cameras in such areas has shown high efficiency.
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Shepelev, Vladimir, Sergei Aliukov, Kseniya Nikolskaya, Arkaprava Das, and Ivan Slobodin. "The Use of Multi-Sensor Video Surveillance System to Assess the Capacity of the Road Network." Transport and Telecommunication Journal 21, no. 1 (February 1, 2020): 15–31. http://dx.doi.org/10.2478/ttj-2020-0002.
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AbstractCurrently, in many cities around the world there is a significant increase in the number of vehicles, which leads to an aggravation of problems and contradictions in the road and transport system. This is especially true of traffic congestion, since the presence of the congestion leads to a number of negative consequences: an increase in travel time, additional fuel consumption and vehicle wear, stress and irritation of drivers and passengers, environmental poisoning and others. To solve the problem of congestion, it is necessary to have a reliable system for collecting information about the situation on the roads and a well-developed method for analyzing the collected information. The paper discusses the possibilities of collecting the required information using multi-touch video cameras and ways to improve them. A distinctive feature of this study is the registration of pedestrians crossing the road at the intersection. The aim of the work is to develop methods for collecting information using road sensor video surveillance systems in a traffic congestion and data processing using statistical methods such as: multiple regression analysis, cluster analysis, multidimensional scaling methods and others. The tasks were set: 1) to identify the most significant factors affecting the intensity of movement of vehicles at intersections in a congestion; 2) divide congestion into clusters with the identification of their characteristics; 3) to give a visual representation of multidimensional statistical information obtained with the help of multi-touch road video cameras.
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Jateikienė, Laura, and Audrius Vaitkus. "AVERAGE SPEED ENFORCEMENT SYSTEM EFFICIENCY ASSESSMENT MODEL." Baltic Journal of Road and Bridge Engineering 12, no. 1 (March 24, 2017): 64–69. http://dx.doi.org/10.3846/bjrbe.2017.08.
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Accidents are one of the leading cause of death all over the world. Speed has been identified as a factor in road accidents, influencing both the risk of accidents and the severity of the accidents. However, speeding is one of the most frequent violations of traffic rules by the road users. Many results of studies showed that the implementation of speed cameras significantly reduced the vehicle speed and the number of accidents near camera sites. One issue regarding speed enforcement by a speed camera is that a reduction in speed is obtained only on a short section of a road. The results of scientific research analysis suggest that this issue can be overcome by the employment of average speed enforcement system (hereafter system), which is a relatively new approach to traffic law enforcement. Until now, Lithuania had no methodology for selecting road sections to be installed with the system. The purpose of this paper is to develop a model for the assessment of safety impact of the system and to perform economic estimation of a system of Lithuanian main road sections selected under this model. The analysis of international practice helped to develop this model, which joins accident indicators, road and traffic parameters. A cost-benefit analysis of the system, implemented on main road sections and selected by suggested model, shows a high level of payback. The implementation of the system would pay back in one year of service.
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Yousaf, Anosha Arooj, Najia Saher, Faisal Shahzad, and Sara Fareed. "A Framework to Reduce Road Congestion by means of Data Analytics." Review of Education, Administration & LAW 4, no. 1 (March 31, 2021): 287–97. http://dx.doi.org/10.47067/real.v4i1.138.
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The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to provide image data as an input to analyze road traffic congestion by counting the number of vehicles under specified interval of time. Monitoring of traffic congestion using image processing techniques is very useful for the future urban road planning such as: 1) if there is a need to make the road wider, 2) if there is a need to add more lanes on the road, 3) if there is need to make flyover or a bridge to control the traffic on the roads. It will help municipalities to structure and expansion of the roads.
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Cafiso, Salvatore, Carmelo D’Agostino, Mariusz Kiec, and Sylwia Pogodzinska. "Application of an Intelligent Transportation System in a Travel Time Information System: Safety Assessment and Management." Transportation Research Record: Journal of the Transportation Research Board 2635, no. 1 (January 2017): 46–54. http://dx.doi.org/10.3141/2635-06.
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The research presented here evaluated road safety on the road sections included in the Intelligent Traffic Control System of the Podhale Region (ISSRRP) in Poland. This travel time information system consists of a remote traffic microwave sensor, cameras, as well as automatic plate number recognition on national roads with variable message signs and a mobile app to suggest alternative routes in the regional road network. The study analyzed changes in safety caused by transferring traffic volume from national to regional rural and suburban road networks. The assessment of the safety performance was performed with an empirical Bayes study, with periods of three years before and after the implementation of ISSRRP. No changes were identified in the safety performance of the national road network after to the introduction of ISSRRP. However, when the overall network is considered, a potential increase in the number of crashes may be expected, depending on the volume of traffic transferred from national to regional roads, and rural or suburban areas. Therefore, a new approach for system management was proposed, taking into account not only improvement in traffic flow, but also safety performance.
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Li, Tianyi, Yuhan Qian, Arnaud de La Fortelle, Ching-Yao Chan, and Chunxiang Wang. "Lane-level localization system using surround-view cameras adaptive to different driving conditions." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142092163. http://dx.doi.org/10.1177/1729881420921630.
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This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.
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West, R. "The effect of speed cameras on injuries from road accidents." BMJ 316, no. 7124 (January 3, 1998): 5–6. http://dx.doi.org/10.1136/bmj.316.7124.5.
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Song, Yongchao, Jieru Yao, Yongfeng Ju, Yahong Jiang, and Kai Du. "Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework." Complexity 2020 (May 14, 2020): 1–17. http://dx.doi.org/10.1155/2020/2435793.
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In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.
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Shaaban, Khaled. "Assessment of Drivers’ Perceptions of Various Police Enforcement Strategies and Associated Penalties and Rewards." Journal of Advanced Transportation 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5169176.
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Road crashes are a major cause of death in many countries. Qatar has been battling to improve road safety on several fronts using different strategies, including road policing. The purpose of this study is to ascertain drivers’ perceptions towards five existing and four proposed police traffic enforcement strategies and associated penalties and rewards in Qatar using face-to-face surveys. The results show that red-light running cameras were perceived to be the most successful existing strategy. The high violation fine and the automation of the system were mentioned as the main reasons for making this strategy the most successful. Three of the existing strategies, fixed-speed enforcement cameras, police enforcement, and mobile speed cameras, were conferred almost the same success percentage, followed by the demerit point system. Regarding the proposed strategies, rewarding safe drivers was selected by the participants as the most successful proposed strategy, followed by introducing more automated enforcement methods. Community service for traffic tickets came in third, followed by defensive driving school. These results can be used to influence future enhancements of existing strategies and guide the development of future traffic strategies being introduced in the traffic system.
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Ziółkowski, Robert. "Identification of road parameters affecting the sectional speed of motor vehicles on rural roads." Transportation Overview - Przeglad Komunikacyjny 2018, no. 11 (November 1, 2018): 1–8. http://dx.doi.org/10.35117/a_eng_18_11_01.
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The basic factors determining the driving speed of drivers on rural roads are geometric properties of the designed road and traffic conditions. Spot speed investigations and analysis are a frequent object of both Polish and foreign research, while little attention has been given to sectional speed studies. The article presents the results of sectional speed tests carried out on rural sections of national, regional and county roads. The research sections were located in the Podlasie Voivodship. A set of cameras with the function of automatic recognition of number plates was used for speed measurements. As part of the conducted research and analyses, applying the tools of statistical analysis, the significance of the influence of the width of the lane, roadside width, road accessibility and its curvature on average sectional speeds in the group of national, provincial and county roads was determined.
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Yamada, Muneo, Koji Ueda, Isao Horiba, Shin Yamamoto, and Sadayuki Tsugawa. "Detection of Wet-Road Conditions from Images Captured by a Vehicle-Mounted Camera." Journal of Robotics and Mechatronics 17, no. 3 (June 20, 2005): 269–76. http://dx.doi.org/10.20965/jrm.2005.p0269.
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This paper discusses the detection of wet-road conditions based on images captured by cameras on the rearview mirror of a vehicle. Based on properties associated with water on a road, detection was stable for daytime and nighttime conditions. Water is recognized on the road based on polarization properties from images. Field tests verified detectability on an expressway at an average 100km/h, with favorable results.
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Pappalardo, Giuseppina, Salvatore Cafiso, Alessandro Di Graziano, and Alessandro Severino. "Decision Tree Method to Analyze the Performance of Lane Support Systems." Sustainability 13, no. 2 (January 16, 2021): 846. http://dx.doi.org/10.3390/su13020846.
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Road departure is one of the main causes of single vehicle and frontal crashes. By implementing lateral support systems, a significant amount of these accidents can be avoided. Typical accidents are normally occurring due to unintentional lane departure where the driver drifts towards and across the line identifying the edge of the lane. The Lane Support Systems (LSS) uses cameras to “read” the lines on the road and alert the driver if the car is approaching the lines. Anyway, despite the assumed technology readiness, there is still much uncertainty regarding the needs of vision systems for “reading” the road and limited results are still available from in field testing. In such framework the paper presents an experimental test of LSS performance carried out in two lane rural roads with different geometric alignments and road marking conditions. LSS faults, in day light and dry pavement conditions, were detected on average in 2% of the road sections. A decision tree method was used to analyze the cause of the faults and the importance of the variable involved in the process. The fault probability increased in road sections with radius less than 200 m and in poor conditions of road marking.
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Postolit, A. V. "Prospects for the Use of Artificial Intelligence and Computer Vision in Transport Systems and Connected Cars." World of Transport and Transportation 19, no. 1 (September 8, 2021): 74–90. http://dx.doi.org/10.30932/1992-3252-2021-19-1-74-90.
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As statistics show, the use of artificial intelligence and machine vision for cars can significantly improve road safety. Even before self-driving vehicles dominate the road, connected vehicles with computer vision-based ADAS will dramatically reduce road accidents. New automotive technologies such as machine vision not only improve road safety, but also open new business opportunities for companies from related industries and sectors such as insurance, car sharing and driver training. Soon, the automotive market, car service services and industries close to transport will change dramatically. This means that those who invest in such developments right now will be able to seize leadership in the era of the spread of innovative technologies.In modern cars, video cameras are used not only as an alternative to the rear-view mirror but are also an important part of active safety systems. Their task, first, is to support collision protection systems through object detection. Cameras are also used to keep track of the lane, to automatically recognise road signs and traffic lights, and monitor the condition of drivers. Together with radars and lidars, they are used to control unmanned vehicles. However, these are far from all the possible areas of useful application of video cameras on board a vehicle. With development of artificial intelligence systems, a decrease in size and an increase in the power of on-board computing facilities, as well as with an increase in the throughput of mobile communications and development of cloud technologies, it becomes possible to implement new services based on video cameras and computer vision.For this reason, the objective of this article is to analyse the trends in development of artificial intelligence, computer vision systems and, considering these trends, to form a list of useful services based on them. The article provides information about what cars can «see» today, how they do it, and what useful services can be implemented for drivers, for transport organisations and for areas related to transport.
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Gaonkar, Needhi U. "Road Traffic Analysis Using Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2002–6. http://dx.doi.org/10.22214/ijraset.2021.37630.
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Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.
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Deng, Xi Xi, Xiao Nian Wang, and Jin Zhu. "Available Lane Detection Based on Radon Transform." Advanced Materials Research 1046 (October 2014): 415–24. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.415.
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To solve lane detection problem in the system of autonomous vehicle, this paper proposes a method of texture segment based on perspective transformation. In this paper, firstly road images were captured through cameras installed on the vehicle, then make a perspective transform to road plane, so that the road and the non-road texture information effectively stand out. After calculation of the texture trend in the transformed image, radon transform can effectively distinguish between the road and the non-road area, and achieve the purpose of the texture regional segment. Experiments prove that this method can be used on the lane detection, which eliminate barriers and road borders effectively.
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Dahmane, Khouloud, Pierre Duthon, Frédéric Bernardin, Michèle Colomb, Frédéric Chausse, and Christophe Blanc. "WeatherEye-Proposal of an Algorithm Able to Classify Weather Conditions from Traffic Camera Images." Atmosphere 12, no. 6 (June 2, 2021): 717. http://dx.doi.org/10.3390/atmos12060717.
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In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new artificial intelligence method that uses road traffic cameras and a convolution neural network to detect weather conditions has, therefore, been proposed. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and order them according to their intensity. The method can differentiate between five weather conditions such as normal (no precipitation), heavy rain, light rain, heavy fog and light fog. The deep-learning method’s training and testing phases were conducted using a new database called the Cerema-AWH (Adverse Weather Highway) database. After several optimisation steps, the proposed method obtained an accuracy of 0.99 for classification.
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Budzynski, Marcin, Kazimierz Jamroz, Jerzy Pyrchla, Wojciech Kustra, Adam Inglot, and Krzysztof Pyrchla. "Automated Parameter Determination for Horizontal Curves for the Purposes of Road Safety Models with the Use of the Global Positioning System." Geosciences 9, no. 9 (September 12, 2019): 397. http://dx.doi.org/10.3390/geosciences9090397.
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This paper presents the results of research conducted to develop an automated system capable of determining parameters for horizontal curves. The system presented in this article could calculate the actual course of a road by means of a two-stage positioning of recorded points along the road. In the first stage, measurements were taken with a Real-Time Network (RTN) receiver installed in a research vehicle. In the second stage, pictures from three cameras, also installed in the vehicle, were analyzed in order to correct the accuracy of the location of the measurement points along the road. The RTN messages and the pictures from the cameras were sent to a mobile workstation which integrated the received signals in an ArcGIS (Esri) environment. The system provides a way to quickly accumulate highly accurate data on the actual geometric parameters of a road. The computer scripts developed by the authors on the basis of the acquired data could automatically determine the parameters of the horizontal curves. The solution was tested in the field and some comments on its advantages and disadvantages are presented in this paper. The automation of data acquisition with regards to the run of a road provides effective data input for mathematical models that include the effect of horizontal curve parameters on road safety. These could be used to implement more effective ways of improving road safety.