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Journal articles on the topic 'Machine vision; Object tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Patil, Rupali, Adhish Velingkar, Mohammad Nomaan Parmar, Shubham Khandhar, and Bhavin Prajapati. "Machine Vision Enabled Bot for Object Tracking." JINAV: Journal of Information and Visualization 1, no. 1 (2020): 15–26. http://dx.doi.org/10.35877/454ri.jinav155.

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Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is do

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Llano, Christian R., Yuan Ren, and Nazrul I. Shaikh. "Object Detection and Tracking in Real Time Videos." International Journal of Information Systems in the Service Sector 11, no. 2 (2019): 1–17. http://dx.doi.org/10.4018/ijisss.2019040101.

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Object and human tracking in streaming videos are one of the most challenging problems in vision computing. In this article, we review some relevant machine learning algorithms and techniques for human identification and tracking in videos. We provide details on metrics and methods used in the computer vision literature for monitoring and propose a state-space representation of the object tracking problem. A proof of concept implementation of the state-space based object tracking using particle filters is presented as well. The proposed approach enables tracking objects/humans in a video, incl

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Zhang, Xiao Jing, Chen Ming Sha, and Ya Jie Yue. "A Fast Object Tracking Approach in Vision Application." Applied Mechanics and Materials 513-517 (February 2014): 3265–68. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3265.

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Object tracking has always been a hot issue in vision application, its application area include video surveillance, human-machine, virtual reality and so on. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences.

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Liu, Liyun. "Moving Object Detection Technology of Line Dancing Based on Machine Vision." Mobile Information Systems 2021 (April 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/9995980.

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In this paper, line dancing's moving object detection technology based on machine vision is studied to improve object detection. For this purpose, the improved frame difference for the background modeling technique is combined with the target detection algorithm. The moving target is extracted, and the postmorphological processing is carried out to make the target detection more accurate. Based on this, the tracking target is determined on the time axis of the moving target tracking stage, the position of the target in each frame is found, and the most similar target is found in each frame of

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Wang, Yongqing, and Yanzhou Zhang. "OBJECT TRACKING BASED ON MACHINE VISION AND IMPROVED SVDD ALGORITHM." International Journal on Smart Sensing and Intelligent Systems 8, no. 1 (2015): 677–96. http://dx.doi.org/10.21307/ijssis-2017-778.

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Jun, Mao. "Object Detection and Recognition Algorithm of Moving UAV Based on Machine Vision." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 7731–37. http://dx.doi.org/10.1166/jctn.2016.5770.

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To conquer disadvantages of slow speed of target tracking algorithm in original distribution field as well as easiness of being caught in local optimal solution, one target tracking algorithm of real-time distribution field based on global matching is presented in the Thesis, thus remarkably improving performance of target tracking algorithm in distribution field. In proposed algorithm, relevant correlation coefficients will be used to substitute the similarity between target distribution filed of original L1 norm measurement and candidate distribution field. As a consequence, the target searc

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Zhang, Zheng, Cong Huang, Fei Zhong, Bote Qi, and Binghong Gao. "Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision." Complexity 2021 (May 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/5526831.

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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an

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Akbari Sekehravani, Ehsan, Eduard Babulak, and Mehdi Masoodi. "Flying object tracking and classification of military versus nonmilitary aircraft." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1394–403. http://dx.doi.org/10.11591/eei.v9i4.1843.

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Tracking of moving objects in a sequence of images is one of the important and functional branches of machine vision technology. Detection and tracking of a flying object with unknown features are important issues in detecting and tracking objects. This paper consists of two basic parts. The first part involves tracking multiple flying objects. At first, flying objects are detected and tracked, using the particle filter algorithm. The second part is to classify tracked objects (military or nonmilitary), based on four criteria; Size (center of mass) of objects, object speed vector, the directio

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Delforouzi, Ahmad, Bhargav Pamarthi, and Marcin Grzegorzek. "Training-Based Methods for Comparison of Object Detection Methods for Visual Object Tracking." Sensors 18, no. 11 (2018): 3994. http://dx.doi.org/10.3390/s18113994.

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Object tracking in challenging videos is a hot topic in machine vision. Recently, novel training-based detectors, especially using the powerful deep learning schemes, have been proposed to detect objects in still images. However, there is still a semantic gap between the object detectors and higher level applications like object tracking in videos. This paper presents a comparative study of outstanding learning-based object detectors such as ACF, Region-Based Convolutional Neural Network (RCNN), FastRCNN, FasterRCNN and You Only Look Once (YOLO) for object tracking. We use an online and offlin

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Aziz, Nor Nadirah Abdul, Yasir Mohd Mustafah, Amelia Wong Azman, et al. "Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review." International Journal on Artificial Intelligence Tools 27, no. 02 (2018): 1830001. http://dx.doi.org/10.1142/s0218213018300016.

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Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking

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Ren, Zi Cheng, Jaeho Choi, M. Ahmed, and Jae Ho Choi. "Scale and Orientation Adaptive Moving Object Tracking in a Sequence of Imageries." Advanced Materials Research 660 (February 2013): 190–95. http://dx.doi.org/10.4028/www.scientific.net/amr.660.190.

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Object tracking has been researched for many years as an important topic in machine learning, robot vision and many other fields. Over the years, various tracking methods are proposed and developed in order to gain a better tracking effect. Among them the mean-shift algorithm turns out to be robust and accurate compared other algorithms after different kinds of tests. But due to its limitations, the changes in scale and rotational motion of an object cannot be effectively processed. This problem occurs when the object of interest moves towards or away from the video camera. Improving over the

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Ekerol, H., and D. C. Hodgson. "A machine vision system for high speed object tracking using a moments algorithm." Mechatronics 2, no. 6 (1992): 555–65. http://dx.doi.org/10.1016/0957-4158(92)90044-o.

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Zou, Yanbiao, Jinchao Li, and Xiangzhi Chen. "Seam tracking investigation via striped line laser sensor." Industrial Robot: An International Journal 44, no. 5 (2017): 609–17. http://dx.doi.org/10.1108/ir-11-2016-0294.

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Purpose This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue. Design/methodology/approach Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc

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Et. al., Mohan kumar Shilpa ,. "An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2506–14. http://dx.doi.org/10.17762/turcomat.v12i2.2098.

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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characterist

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Gupta, Arpit. "Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5452–58. http://dx.doi.org/10.22214/ijraset.2021.36144.

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Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and t

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Ciric, Ivan, Zarko Cojbasic, Vlastimir Nikolic, Tomislav Igic, and Branko Tursnek. "Intelligent optimal control of thermal vision-based Person-Following Robot Platform." Thermal Science 18, no. 3 (2014): 957–66. http://dx.doi.org/10.2298/tsci1403957c.

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In this paper the supervisory control of the Person-Following Robot Platform is presented. The main part of the high level control loop of mobile robot platform is a real-time robust algorithm for human detection and tracking. The main goal was to enable mobile robot platform to recognize the person in indoor environment, and to localize it with accuracy high enough to allow adequate human-robot interaction. The developed computationally intelligent control algorithm enables robust and reliable human tracking by mobile robot platform. The core of the recognition methods proposed is genetic opt

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Ahmad, Misbah, Imran Ahmed, Fakhri Alam Khan, Fawad Qayum, and Hanan Aljuaid. "Convolutional neural network–based person tracking using overhead views." International Journal of Distributed Sensor Networks 16, no. 6 (2020): 155014772093473. http://dx.doi.org/10.1177/1550147720934738.

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In video surveillance, person tracking is considered as challenging task. Numerous computer vision, machine and deep learning–based techniques have been developed in recent years. Majority of these techniques are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking. The network layers of convolutional neural network models trained on a number of images or video sequences improve speed and accuracy of object tracking. In this work, the generalization performance of existing pre-trained deep learning models have investig

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Yi, Yugen, Jiangyan Dai, Chengduan Wang, et al. "An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection." Applied Sciences 9, no. 23 (2019): 5042. http://dx.doi.org/10.3390/app9235042.

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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features

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Wei, Zhao, and Wei YongBin. "Non-contact measurement method of bridge deflection based on machine vision." E3S Web of Conferences 261 (2021): 02001. http://dx.doi.org/10.1051/e3sconf/202126102001.

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Bridge deflection is a very important parameter for bridge structure. It directly reflects the vertical overall stiffness of bridge structure, and is an important basis for reflecting the linear change of bridge. It is closely related to the bearing capacity of the bridge and the ability to resist earthquake and other dynamic loads. Computer vision is to obtain image data through image acquisition device, and use special image processing software to get the shape and displacement information of the object. This method has high precision, low cost, and simple operation. In this paper, based on

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Zhang, Shunli, Wei Lu, Weiwei Xing, and Li Zhang. "Using fuzzy least squares support vector machine with metric learning for object tracking." Pattern Recognition 84 (December 2018): 112–25. http://dx.doi.org/10.1016/j.patcog.2018.07.012.

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Wei, Bingsheng, and Martin Barczyk. "Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging." Drones 5, no. 2 (2021): 37. http://dx.doi.org/10.3390/drones5020037.

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We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accura

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Bahraini, Masoud S., Ahmad B. Rad, and Mohammad Bozorg. "SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm." Sensors 19, no. 17 (2019): 3699. http://dx.doi.org/10.3390/s19173699.

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The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracki

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López-Sastre, Roberto, Carlos Herranz-Perdiguero, Ricardo Guerrero-Gómez-Olmedo, Daniel Oñoro-Rubio, and Saturnino Maldonado-Bascón. "Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor." Sensors 19, no. 19 (2019): 4062. http://dx.doi.org/10.3390/s19194062.

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In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewp

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Zhu, Li, and Hang Hu. "Research of Motion Tracking Based on CamShift Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2403.

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At present, objects detection, identification and tracking according to machine vision technology, have been widely used in various economic aspects. In this paper, a real-time monitoring system was discussed. This surveillance system mainly combines two sub-systems, motion detection and objects tracking. An Adaptive Gaussian Background Model was established in order to automatically update the background and detect the outline of moving objects. By analyzing different algorithms, this paper brings out approaches to promote the performance.We proposed CamShift algorithm to complete motion dete

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Chen, Joy Iong-Zong, and Jen-Ting Chang. "Applying a 6-axis Mechanical Arm Combine with Computer Vision to the Research of Object Recognition in Plane Inspection." June 2020 2, no. 2 (2020): 77–99. http://dx.doi.org/10.36548/jaicn.2020.2.002.

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The study of a robotic arm copied with 3D-printer combines computer vision system with tracking algorithm is proposed in the paper. Moreover, the designing to the intelligent vehicle system with the integration of electromechanical for planning to apply it to the operations in various fields is presented too. The main purpose of this work tries to avoid the complicated process with traditional manual adjustment or teaching. It is expected to achieve the purpose that the robotic arm can grab the target automatically, classify the target and place it in the specified area, and even accurately re

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Winmalar D, Haretha, Vani A K, Sudharsan R, and Hari Krishna R. "Generalized Omnipresence Detection (GOD)." Journal of Innovative Image Processing 2, no. 2 (2020): 85–92. http://dx.doi.org/10.36548/jiip.2020.2.003.

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Identification and Tracking of a person in a video are useful in applications such as video surveillance. Two levels of tracking are carried out. They are Classification and monitoring of individuals. The human body’s color histogram is used as the basis for monitoring individuals. Our project can detect a human face in a video and store the detected facial features of the Local Binary Pattern Histogram (LBPH). In a video, once a person is detected, it automatically track that individual and assigns a label to that individual. We use the stored LBPH features to track him in any other videos. I

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Konwar, Lakhyadeep, Anjan Kumar Talukdar, Kandarpa Kumar Sarma, Navajit Saikia, and Subhash Chandra Rajbangshi. "Segmentation and Selective Feature Extraction for Human Detection to the Direction of Action Recognition." International Journal of Circuits, Systems and Signal Processing 15 (September 8, 2021): 1371–86. http://dx.doi.org/10.46300/9106.2021.15.147.

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Detection as well as classification of different object for machine vision application is a challenging task. Similar to the other object detection and classification task, human detection concept provides a major role for the ad- vancement in the design of an automatic visual surveillance system (AVSS). For the future automation system if it is possible to include human detection and tracking, human action recognition, usual as well as unusual event recognition etc. concept for future AVSS, it will be a greater success in the transformable world. In this paper we have proposed a proper human

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LANG, HAOXIANG, and CLARENCE W. DE SILVA. "FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION." International Journal of Information Acquisition 05, no. 02 (2008): 93–110. http://dx.doi.org/10.1142/s0219878908001521.

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In this paper, a four layer neuro-fuzzy architecture of multi-sensor fusion is developed for a fault diagnosis system which is applied to an industrial fish cutting machine. An important characteristic of the fault diagnosis approach developed in this paper is to make an accurate decision of the machine condition by fusing information acquired from three types of sensors: Accelerometer, microphone and charge-coupled device (CCD) camera. Feature vectors for vibration and sound signals from their fast Fourier transform (FFT) frequency spectra are defined and extracted from the acquired informati

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Chang, Ching-Yuan, En-Chieh Chang, and Chi-Wen Huang. "In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology." Sensors 19, no. 24 (2019): 5340. http://dx.doi.org/10.3390/s19245340.

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This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displac

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Jose, John Anthony C., Meygen D. Cruz, Jefferson James U. Keh, Maverick Rivera, Edwin Sybingco, and Elmer P. Dadios. "Anno-Mate: Human–Machine Collaboration Features for Fast Annotation." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (2021): 404–9. http://dx.doi.org/10.20965/jaciii.2021.p0404.

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Large annotated datasets are crucial for training deep machine learning models, but they are expensive and time-consuming to create. There are already numerous public datasets, but a vast amount of unlabeled data, especially video data, can still be annotated and leveraged to further improve the performance and accuracy of machine learning models. Therefore, it is essential to reduce the time and effort required to annotate a dataset to prevent bottlenecks in the development of this field. In this study, we propose Anno-Mate, a pair of features integrated into the Computer Vision Annotation To

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Ciccotelli, Joseph, Michel Dufaut, and René Husson. "Control of tracking systems by image correlation." Robotica 5, no. 3 (1987): 201–6. http://dx.doi.org/10.1017/s0263574700015848.

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SUMMARYOwing to advances in machine vision, it is now possible to study automatic gripping of moving parts. This complex task requires a precise knowledge of the displacements of objects in a camera field.In this paper, a method to analyse the motion of parts is presented; it is based on the correlation of numerical images. The treatment of data provided by the image background makes this method quite original.The utilization of this method, often considered as rather awkward, makes it possible, in this case, to develop a position feedback operation of the robot actuators controlled in an open

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Volkov, Vladimir Yu, Oleg A. Markelov, and Mikhail I. Bogachev. "IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING." Journal of the Russian Universities. Radioelectronics 22, no. 3 (2019): 24–35. http://dx.doi.org/10.32603/1993-8985-2019-22-3-24-35.

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Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials a

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Sadeghi-Niaraki, Abolghasem, and Soo-Mi Choi. "A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems." Sensors 20, no. 10 (2020): 2997. http://dx.doi.org/10.3390/s20102997.

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Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object

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Gunay, Noel S., Elmer P. Dadios, Ryan Rhay P. Vicerra, Argel A. Bandala, and Laurence A. Gan Lim. "Synchronized Dual Camera Vision System for Locating and Identify Highly Dynamic Objects." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 5 (2014): 776–83. http://dx.doi.org/10.20965/jaciii.2014.p0776.

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This paper presents machine vision for locating and identifying 23 highly dynamic objects on 4.4 meters by 2.8 meters micro robot soccer playing field. The approach is based from the idea that the two camera vision subsystems should be synchronized and well informed in real time of the combined vision data and a selection of objects to track under each other’s camera view. A measure of effectiveness on using incremental tracking for two-camera operation is developed and is used to evaluate the introduced approach through experimentation. A real-time visualization of the whole playfield contain

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Othman, Zuraini, Asmala Ahmad, Fauziah Kasmin, Sharifah Sakinah Syed Ahmad, Mohd Yazid Abu Sari, and Muhammad Amin Mustapha. "Comparison between Edge Detection Methods on UTeM Unmanned Arial Vehicles Images." MATEC Web of Conferences 150 (2018): 06029. http://dx.doi.org/10.1051/matecconf/201815006029.

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Machine vision calls for the use of detectors to ascertain the features and type of object portrayed in the image. The employment of unmanned aerial vehicles (UAVs), which can function freely in active and precarious settings, is currently gaining momentum. These vehicles are mainly used for the detecting, classifying and tracking of an object. However, the achievement of these objectives necessitates the involvement of an effective edge detection procedure. Sobel, Canny, Prewitt and LoG are among the many edge detection procedures presently available. In this endeavour, we opted for the utili

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Pandian, A. Pasumpon. "Review on Image Recoloring Methods for Efficient Naturalness by Coloring Data Modeling Methods for Low Visual Deficiency." September 2021 3, no. 3 (2021): 169–83. http://dx.doi.org/10.36548/jaicn.2021.3.002.

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Recent research has discovered new applications for object tracking and identification by simulating the colour distribution of a homogeneous region. The colour distribution of an object is resilient when it is subjected to partial occlusion, scaling, and distortion. When rotated in depth, it may remain relatively stable in other applications. The challenging task in image recoloring is the identification of the dichromatic color appearance, which is remaining as a significant requirement in many recoloring imaging sectors. This research study provides three different vision descriptions for i

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Feng, Zhanshen. "An Image Detection Method Based on Parameter Optimization of Support Vector Machine." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 306–14. http://dx.doi.org/10.46300/9106.2021.15.35.

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With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, sem

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Zhao, Qi, Boxue Zhang, Shuchang Lyu, et al. "A CNN-SIFT Hybrid Pedestrian Navigation Method Based on First-Person Vision." Remote Sensing 10, no. 8 (2018): 1229. http://dx.doi.org/10.3390/rs10081229.

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The emergence of new wearable technologies, such as action cameras and smart glasses, has driven the use of the first-person perspective in computer applications. This field is now attracting the attention and investment of researchers aiming to develop methods to process first-person vision (FPV) video. The current approaches present particular combinations of different image features and quantitative methods to accomplish specific objectives, such as object detection, activity recognition, user–machine interaction, etc. FPV-based navigation is necessary in some special areas, where Global Po

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Oyekan, John, Axel Fischer, Windo Hutabarat, Christopher Turner, and Ashutosh Tiwari. "Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence." Assembly Automation 40, no. 6 (2019): 925–39. http://dx.doi.org/10.1108/aa-06-2018-078.

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Purpose The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques. Design/methodology/approach This research exploits RGB-D cameras such as Kinect® to investigate the fe

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Maltezos, Evangelos, Athanasios Douklias, Aris Dadoukis, et al. "The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets." Computation 9, no. 2 (2021): 12. http://dx.doi.org/10.3390/computation9020012.

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Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become a

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Ciric, Ivan, Zarko Cojbasic, Danijela Ristic-Durrant, et al. "Thermal vision based intelligent system for human detection and tracking in mobile robot control system." Thermal Science 20, suppl. 5 (2016): 1553–59. http://dx.doi.org/10.2298/tsci16s5553c.

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This paper presents the results of the authors in thermal vision based mobile robot control. The most important segment of the high level control loop of mobile robot platform is an intelligent real-time algorithm for human detection and tracking. Temperature variations across same objects, air flow with different temperature gradients, reflections, person overlap while crossing each other, and many other non-linearities, uncertainty and noise, put challenges in thermal image processing and therefore the need of computationally intelligent algorithms for obtaining the efficient performance fro

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Opromolla, Roberto, Giuseppe Inchingolo, and Giancarmine Fasano. "Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning." Sensors 19, no. 19 (2019): 4332. http://dx.doi.org/10.3390/s19194332.

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The performance achievable by using Unmanned Aerial Vehicles (UAVs) for a large variety of civil and military applications, as well as the extent of applicable mission scenarios, can significantly benefit from the exploitation of formations of vehicles able to fly in a coordinated manner (swarms). In this respect, visual cameras represent a key instrument to enable coordination by giving each UAV the capability to visually monitor the other members of the formation. Hence, a related technological challenge is the development of robust solutions to detect and track cooperative targets through a

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Zhang, Shu Shan, and Juan P. Wachs. "The Improvement and Application of Intelligence Tracking Algorithm for Moving Logistics Objects Based on Machine Vision Sensor." Sensor Letters 11, no. 5 (2013): 862–69. http://dx.doi.org/10.1166/sl.2013.2658.

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Luo, ZongWei, Martin Lai, Mary Cheung, et al. "Developing Local Association Network Based IoT Solutions for Body Parts Tagging and Tracking." International Journal of Systems and Service-Oriented Engineering 1, no. 4 (2010): 42–64. http://dx.doi.org/10.4018/jssoe.2010100104.

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Traditional Internet is commonly wired with machine to machine persistent connections. Evolving towards mobile and wireless pervasive networks, Internet has to entertain dynamic, transient, and changing interconnections. The vision of the Internet of Things furthers technology development by creating an interactive environment where smart objects are connected and can sense and react to the environment. Adopting such an innovative technology often requires extensive intelligence research. A major value indicator is how the potentials of RFID can translate into actions to improve business opera

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Merrad, Yacine, Mohamed Hadi Habaebi, Md Rafiqul Islam, and Teddy Surya Gunawan. "A Real-time Mobile Notification System for Inventory Stock out Detection using SIFT and RANSAC." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 05 (2020): 32. http://dx.doi.org/10.3991/ijim.v14i05.13315.

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<p>Object detection and tracking is one of the most relevant computer technologies related to computer vision and image processing. It may mean the detection of an object within a frame and classify it (human, animal, vehicle, building, etc) by the use of some algorithms. It may also be the detection of a reference object within different frames (under different angles, different scales, etc.). The applications of the object detection and tracking are numerous; most of them are in the security field. It is also used in our daily life applications, especially in developing and enhancing b

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Taha, Mohammed Yaseen, and Qahhar Muhammad Qadir. "Cost Effective and Easily Configurable Indoor Navigation System." UKH Journal of Science and Engineering 5, no. 1 (2021): 60–72. http://dx.doi.org/10.25079/ukhjse.v5n1y2021.pp60-72.

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With the advent of Industry 4.0, the trend of its implementation in current factories has increased tremendously. Using autonomous mobile robots that are capable of navigating and handling material in a warehouse is one of the important pillars to convert the current warehouse inventory control to more automated and smart processes to be aligned with Industry 4.0 needs. Navigating a robot’s indoor positioning in addition to finding materials are examples of location-based services (LBS), and are some major aspects of Industry 4.0 implementation in warehouses that should be considered. Global p

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Pham, Tuan-Anh, and Myungsik Yoo. "Nighttime Vehicle Detection and Tracking with Occlusion Handling by Pairing Headlights and Taillights." Applied Sciences 10, no. 11 (2020): 3986. http://dx.doi.org/10.3390/app10113986.

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In recent years, vision-based vehicle detection has received considerable attention in the literature. Depending on the ambient illuminance, vehicle detection methods are classified as daytime and nighttime detection methods. In this paper, we propose a nighttime vehicle detection and tracking method with occlusion handling based on vehicle lights. First, bright blobs that may be vehicle lights are segmented in the captured image. Then, a machine learning-based method is proposed to classify whether the bright blobs are headlights, taillights, or other illuminant objects. Subsequently, the det

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Uddin, Machbah, Hira Lal Gope, Md Sayeed Iftekhar Yousuf, Dilshad Islam, and Mohammad Khairul Islam. "Crowd Detection in Still Images Using Combined HOG and SIFT Feature." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (2016): 447. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp447-458.

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<p>Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd. Individual object detection has been improved significantly in recent times but the crowd detection and tracking contains some challenges. Crowd analysis is a highly focused area for law enforcement, urban engineering and traffic management. There are a lot of incident occurred in crowd area during some fabulous event. In this research low resolution and verities of image orientation is a key factor as well as overlapping person images

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HAN, LONG, XINYU WU, YONGSHENG OU, YEN-LUN CHEN, CHUNJIE CHEN, and YANGSHENG XU. "HOUSEHOLD SERVICE ROBOT WITH CELLPHONE INTERFACE." International Journal of Information Acquisition 09, no. 02 (2013): 1350009. http://dx.doi.org/10.1142/s0219878913500095.

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In this paper, an efficient and low-cost cellphone-commandable mobile manipulation system is described. Aiming at house and elderly caring, this system can be easily commanded through common cellphone network to efficiently grasp objects in household environment, utilizing several low-cost off-the-shelf devices. Unlike the visual servo technology using high quality vision system with high cost, the household-service robot may not afford to such high quality vision servo system, and thus it is essential to use some of low-cost device. However, it is extremely challenging to have the said vision

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Bouteraa, Yassine, and Ismail Ben Abdallah. "A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp." Industrial Robot: An International Journal 44, no. 5 (2017): 575–87. http://dx.doi.org/10.1108/ir-12-2016-0356.

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Purpose The idea is to exploit the natural stability and performance of the human arm during movement, execution and manipulation. The purpose of this paper is to remotely control a handling robot with a low cost but effective solution. Design/methodology/approach The developed approach is based on three different techniques to be able to ensure movement and pattern recognition of the operator’s arm as well as an effective control of the object manipulation task. In the first, the methodology works on the kinect-based gesture recognition of the operator’s arm. However, using only the vision-ba

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