Journal articles: 'Rule-Based Moving Object Tracking'

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Hashimoto, Masafumi, Yosuke Matsui, and Kazuhiko Takahashi. "Moving-Object Tracking with In-Vehicle Multi-Laser Range Sensors." Journal of Robotics and Mechatronics 20, no. 3 (June 20, 2008): 367–77. http://dx.doi.org/10.20965/jrm.2008.p0367.

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This paper presents a method for moving-object tracking with in-vehicle 2D laser range sensor (LRS) in a cluttered environment. A sensing area of one LRS is limited in orientation, and hence the mobile robot is equipped with multi-LRSs for omnidirectional sensing. Since each LRS takes the laser image on its own local coordinate frame, the laser image is mapped onto a reference coordinate frame so that the object tracking can be achieved by cooperation of multi-LRSs. For mapping the coordinate frames of multi-LRSs are calibrated, that is, the relative positions and orientations of the multi-LRSs are estimated. The calibration is based on Kalman filter and chi-hypothesis testing. Moving-object tracking is achieved by two steps: detection and tracking. Each LRS finds moving objects from its own laser image via a heuristic rule and an occupancy grid based method. It tracks the moving objects via Kalman filter and the assignment algorithm based data association. When the moving objects exist in the overlapped sensing areas of the LRSs, these LRSs exchange the tracking data and fuse them in a decentralized manner. A rule based track management is embedded into the tracking system in order to enhance the tracking performance. The experimental result of three walking-people tracking in an indoor environment validates the proposed method.

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Yuan, Bao Hong, De Xiang Zhang, Kui Fu, and Ling Jun Zhang. "Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter." Applied Mechanics and Materials 380-384 (August 2013): 3672–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3672.

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In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.

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Jang, Dae-Sik, Gye-Young Kim, and Hyung-Il Choi. "Model-based tracking of moving object." Pattern Recognition 30, no. 6 (June 1997): 999–1008. http://dx.doi.org/10.1016/s0031-3203(96)00128-8.

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Choe, Gwangmin, Tianjiang Wang, Fang Liu, Gwangho Li, Hyongwang O, and Songryong Kim. "Moving object tracking based on geogram." Multimedia Tools and Applications 74, no. 21 (June 26, 2014): 9771–94. http://dx.doi.org/10.1007/s11042-014-2150-8.

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Fan, Jian De, and Jiang Bo Zhu. "Object Tracking Based on Dual-View Stereo System." Advanced Materials Research 850-851 (December 2013): 780–83. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.780.

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Tracking moving objects in dual-view stereo system is becoming a hot research area in computer vision. To capture the moving objects pixels more accurately, we proposed a new object tracking algorithm which first compute moving objects feature points and then match these points, finally connect the matching feature points and get objects motion trajectories. The algorithm was tested in the video sequences with resolution 640×480 and 768×576 individually. The results show that the algorithm is more robust and the trajectories of the moving objects tracked with our method are more accurate compared with current method of L-K optical flow.

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Zhang, Ming Jie, and Bao Sheng Kang. "An Improved Moving Object Tracking Method Based on Graph Cuts." Applied Mechanics and Materials 596 (July 2014): 398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.596.398.

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In order to improve efficiency of object tracking in occlusion states. A method to detect and automatically track was present in a surveillance system. Firstly, a graph cuts method was employed to segment image from a static scene. To identify foreground objects by positions and sizes of the obtained foreground regions. In addition, the performance to track objects was improved by using the improved overlap tracking method, the tracking method was used to analyze the centroid distance between neighboring objects and help object tracking in occlusion states of merging and splitting. By the experiments of moving object tracking in three video sequences, the experimental results exhibit that the proposed method is better than the traditional method.

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Fan, Jianping, Essam A. El-Kwae, Mohand-Said Hacid, and Feng Liang. "Novel tracking-based moving object extraction algorithm." Journal of Electronic Imaging 11, no. 3 (2002): 393. http://dx.doi.org/10.1117/1.1482095.

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Wang, Qun Long. "Human Motion Tracking Based on B-Spline Snake Algorithm." Advanced Materials Research 706-708 (June 2013): 1886–89. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1886.

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A new method for tracking of moving human objects is presented. The developed algorithm is based on B-spline Snake model. The Snake algorithm accesses the object contour through minimizing the energy; the object contour in each frame is presented through three B-spline curve. This approach gets the change image with double thresholds image segmentation between the neighboring frames, it could detect moving human objects with high quality and locate objects approximately. The contour extracts from the last frame put at the approximate position as the B-spline Snake model initialization. It accomplishes the tracking of moving human objects.

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Gao, Tao. "Data Association Based Tracking Traffic Objects." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (April 2013): 31–46. http://dx.doi.org/10.4018/japuc.2013040104.

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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system.

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Wang, Zhen Hai, and Ki Cheon Hong. "An New Method for Multi-Object Tracking Using Energy Minimization-Based Data Association." Applied Mechanics and Materials 427-429 (September 2013): 1822–25. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1822.

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multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.

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Li, Dong Mei, Tao Li, Tao Xiang, and Wei Xu. "Multiple Objects Tracking Based on Linear Fitting." Applied Mechanics and Materials 602-605 (August 2014): 1438–41. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1438.

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For multiple objects tracking in complex scenes, a new tracking algorithm based on linear fitting for multiple moving objects is proposed. DG_CENTRIST feature and color feature are combined to describe the object, and the overlapping ratio of the tracking object is calculated. The object in the current frame is measured by using coincidence degree. If there is occlusion, we predict the path of each object by linear fitting and adjust the results of tracking in order to get correct results. The experiment results show that this method can effectively improve the accuracy of the multiple target tracking.

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Liu, Tao, and Yong Liu. "Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels." Journal of Advanced Transportation 2021 (July 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/8153474.

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Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking.

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Ha, Ngo Duong, Ikuko Shimizu, and Pham The Bao. "Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information." Computational Intelligence and Neuroscience 2020 (December 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/8839725.

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Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video’s timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.

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Wang, Yanjiang, Yujuan Qi, and Yongping Li. "Memory-Based Multiagent Coevolution Modeling for Robust Moving Object Tracking." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/793013.

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The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods.

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Montecillo-Puente, Francisco-Javier, and Victor Ayala-Ramirez. "Object Tracking based on Fuzzy Color Blobs." Acta Universitaria 22 (March 1, 2012): 27–34. http://dx.doi.org/10.15174/au.2012.338.

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One of the mayor goals in computer vision is object representation. Object representation aims to determine a set of features that best represents a specific object in an image, for example interest points, edges, color and texture. However, objects are generally composed of several regions containing different information which is more or less convenient to be represented by one of these features. Furthermore, each of these regions could be static or moving with respect to each other. In this sense, this paper presents an object representation based on fuzzy color blobs and spatial relationships among them. This approach of object representation is used to track rigid and articulated objects.

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Biswas, Debojit, Hongbo Su, Chengyi Wang, and Aleksandar Stevanovic. "Speed Estimation of Multiple Moving Objects from a Moving UAV Platform." ISPRS International Journal of Geo-Information 8, no. 6 (May 31, 2019): 259. http://dx.doi.org/10.3390/ijgi8060259.

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Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.

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Li, Dong Mei, and Tao Li. "Multiple Objects Tracking Based on Mixture Features." Advanced Materials Research 945-949 (June 2014): 1869–74. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1869.

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For multiple objects tracking in complex scenes, this paper proposes a new tracking algorithm for multiple moving objects. The algorithm makes likelihood calculation by using new DG_CENTRIST feature and color feature, and then calculates the overlapping ratio of the tracking object and the object in the current frame using coincidence degree to measure the occlusion. The algorithm has good robustness and stability, and the experiment results show that this method can effectively improve the accuracy of the multiple target tracking.

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YU, Yong. "Infrared moving object tracking based on particle filter." Journal of Computer Applications 28, no. 6 (August 20, 2008): 1543–45. http://dx.doi.org/10.3724/sp.j.1087.2008.01543.

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Kim, Bongjae, Hong Min, Junyoung Heo, and Jinman Jung. "Dynamic Computation Offloading Scheme for Drone-Based Surveillance Systems." Sensors 18, no. 9 (September 6, 2018): 2982. http://dx.doi.org/10.3390/s18092982.

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Recently, various technologies for utilizing unmanned aerial vehicles have been studied. Drones are a kind of unmanned aerial vehicle. Drone-based mobile surveillance systems can be applied for various purposes such as object recognition or object tracking. In this paper, we propose a mobility-aware dynamic computation offloading scheme, which can be used for tracking and recognizing a moving object on the drone. The purpose of the proposed scheme is to reduce the time required for recognizing and tracking a moving target object. Reducing recognition and tracking time is a very important issue because it is a very time critical job. Our dynamic computation offloading scheme considers both the dwell time of the moving target object and the network failure rate to estimate the response time accurately. Based on the simulation results, our dynamic computation offloading scheme can reduce the response time required for tracking the moving target object efficiently.

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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 the video sequence. The association relationship is established to determine a moving object template or feature. Through certain measurement criteria, the mean-shift algorithm is used to search the optimal candidate target in the image frame and carry out the corresponding matching to realize moving objects' tracking. This method can detect the moving targets of line dancing in various areas through the experimental analysis, which will not be affected by the position or distance, and always has a more accurate detection effect.

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Krerngkamjornkit, Rapee, and Milan Simic. "Multi Object Detection and Tracking from Video File." Applied Mechanics and Materials 533 (February 2014): 218–25. http://dx.doi.org/10.4028/www.scientific.net/amm.533.218.

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This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

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JIANG, MING-XIN, ZHI-JING SHAO, and HONG-YU WANG. "REAL-TIME OBJECT TRACKING ALGORITHM WITH CAMERAS MOUNTED ON MOVING PLATFORMS." International Journal of Image and Graphics 12, no. 03 (July 2012): 1250020. http://dx.doi.org/10.1142/s0219467812500209.

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Object tracking is one of the key techniques in computer vision. Present algorithms are mainly implemented in static platforms. In this paper, we propose a novel technique for real-time object tracking in videos captured by cameras on moving platforms. First, we rule out feature points that have optical flows inconsistent with those of background. Second, optical flows on the rest of the feature points are utilized to estimate the global motion of the camera. Finally, the kinematic function of particle filtering is modified by the global motion of the camera, together with color-space histogram as appearance model, to achieve robustness in unstable video sequences. The proposed algorithm is tested on several video sequences, compared to mean-shift algorithm and traditional particle filtering tracking, it shows promising real-time tracking performance. Experiments demonstrate that our algorithm can track moving object robustly in videos captured by moving cameras.

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Aziz, Nor Nadirah Abdul, Yasir Mohd Mustafah, Amelia Wong Azman, Amir Akramin Shafie, Muhammad Izad Yusoff, Nor Afiqah Zainuddin, and Mohammad Ariff Rashidan. "Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review." International Journal on Artificial Intelligence Tools 27, no. 02 (March 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, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance.

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Kong, Hongshan, and Bin Yu. "A Moving Object Indoor Tracking Model Based on Semiactive RFID." Mathematical Problems in Engineering 2018 (December 25, 2018): 1–7. http://dx.doi.org/10.1155/2018/4812057.

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Aimed at the weak anti-interference and low accuracy problem of moving object indoor tracking based RFID, a moving object indoor tracking model based on semiactive RFID is presented. This model acquires scene location information through RFID low frequency triggers preinstalled, which can enhance the anti-interference ability. This model adopts an improved particle filter algorithm, which can increase the diversity of the particles, overcome the particle impoverishment, and reduce the tracking error. Simulation results indicate that the model can achieve better tracking performances. Compared with standard particle filter, the improved algorithm performance is better in the capability of tracking accuracy and robust and is more suitable for indoor tracking application in the complicated environments.

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Ma, Yuchi, John Anderson, Stephen Crouch, and Jie Shan. "Moving Object Detection and Tracking with Doppler LiDAR." Remote Sensing 11, no. 10 (May 14, 2019): 1154. http://dx.doi.org/10.3390/rs11101154.

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In this paper, we present a model-free detection-based tracking approach for detecting and tracking moving objects in street scenes from point clouds obtained via a Doppler LiDAR that can not only collect spatial information (e.g., point clouds) but also Doppler images by using Doppler-shifted frequencies. Using our approach, Doppler images are used to detect moving points and determine the number of moving objects followed by complete segmentations via a region growing technique. The tracking approach is based on Multiple Hypothesis Tracking (MHT) with two extensions. One is that a point cloud descriptor, Oriented Ensemble of Shape Function (OESF), is proposed to evaluate the structure similarity when doing object-to-track association. Another is to use Doppler images to improve the estimation of dynamic state of moving objects. The quantitative evaluation of detection and tracking results on different datasets shows the advantages of Doppler LiDAR and the effectiveness of our approach.

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Hatimi, H., M. Fakir, M. Chabi, and M. Najimi. "New Approach for Detecting and Tracking a Moving Object." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3296. http://dx.doi.org/10.11591/ijece.v8i5.pp3296-3303.

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<span>This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.</span>

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Cao, Song Xiao, Xuan Yin Wang, Xiao Jie Fu, and Ke Xiang. "Servo Tracking of Moving Object Based on Particle Filter." Advanced Materials Research 271-273 (July 2011): 1130–35. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1130.

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We present a servo control model in a particle filter to realize robust visual object tracking. Particle filter has attracted much attention due to its robust tracking performance in cluttered environments. However, most methods are in the mode of moving object and stationary camera, as a result, the tracking will become failure if the object goes out of the field of view of the camera. In this paper, a closed loop control model based on speed regulation is proposed to drive the pan/tilt/zoom(PTZ) camera to keep the target in the center of the camera angle. The experiment result shows that our system can track the moving object well, and can always keep the object in the middle of the field of view. The system is computationally efficient and can run in real-time completely.

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Sun, Ming, and Jia Wei Li. "Object Tracking Algorithm Based on Block LAB Feature Histogram and Particle Filter." Advanced Materials Research 485 (February 2012): 193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.485.193.

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In order to improve real-time object tracking effect when tracking objects are partly covered or mixed by different backgrounds, and even under the conditions of changed illuminations, in this paper, we proposed an object tracking algorithm based on block LAB feature histogram and particle filter. To demonstrate new algorithm’s excellent performance, we carried several compared experiments when objects moved under different conditions such as changed illuminations, mixed backgrounds and so forth. Experiment results show that tracking objects are often lost by using tracking algorithm based on traditional features such as color histogram, but moving objects under various and complex environments could be correctly tracked by using real-time tracking algorithm proposed in this paper.

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Khalifa, Othman Omran, Norun Abdul Malek, and Kazi Istiaque Ahmed. "Robust Vision-based Multiple Moving Object Detection and Tracking from Video Sequences." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 817. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp817-826.

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<span lang="EN-US">Detection of Moving Objects and Tracking is one of the most concerned issue and is being vastly used at home, business and modern applications. It is used to identify and track of an entity in a significant way. This paper illustrates the way to detect multiple objects using background subtraction methods and extract each object features by using Speed-Up Robust Feature algorithm and track the features through k-Nearest Neighbor processing from different surveillance videos sequentially. In the detection of object of each frame, pixel difference is calculated with respect to the reference background frame for the detection of an object which is only suitable for any ideal static condition with the consideration of lights from the environment. Thus, this method will detect the complete object and the extracted feature will be carried out for the tracking of the object in the multiple videos by one by one video. It is expected that this proposed method can commendably abolish the impact of the changing of lights</span>

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Yang, X., F. Li, M. Lu, L. Xin, X. Lu, and N. Zhang. "MOVING OBJECT DETECTION METHOD OF VIDEO SATELLITE BASED ON TRACKING CORRECTION DETECTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 701–7. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-701-2020.

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Abstract. It is the focus of current research that how to realize high precision and real-time dynamic monitoring and tracking of moving targets by video satellites because of instantaneous and dynamic continuous observation of targets in a certain area by the video satellites. The existing detection and tracking methods for moving objects have target misdetection and missed detection, which reduces the accuracy of moving object detection. In this paper, a Tracking Correction Detection Correction (TCD) method is proposed to solve these problems. Firstly, the background model is established by using the improved ViBe target detection algorithm, and the moving target mask is obtained by adaptive threshold calculation. By using pyramid structure iterative algorithm, the moving object can be classified as noise or real object according to the set of detection results of different detection windows. The high-order correlation vector tracking method is used to modify the detection result of the moving target acquired in the previous frame, and finally the accurate detection result of the moving target is obtained. The comparison analysis between the frame difference (FD) method, GMM method, ViBe method and TCD method shows that the TCD method has better robustness for noise, light and background dynamic changes, and the test results of TCD method are more complete and the real-time is better. It is proved by this work that the accuracy of the target detection of TCD method has reached 85%, which has a high engineering application value.

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Guo, Shi Xu, Jia Xin Chen, and Bo Peng. "Research of Object Tracking Algorithm Based on BRISK." Advanced Materials Research 1049-1050 (October 2014): 1496–501. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1496.

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In view of the problems that high complexity, large calculation and the difficulty to apply to real-time systems in the current moving target tracking algorithm, this paper introduce the BRISK feature extraction algorithm, and proposed the object tracking algorithm based on BRISK. Set up the background model and use the background difference method to detect the moving target template. Then match in the next frame and track the target. In order to reduce the search feature matching area, further improve the real-time of the algorithm, we also introduce the kalman filter algorithm to estimate the target motion trajectory. The experimental result show that comparing with the SURF, SIFT feature tracking algorithm, the algorithm of this paper has greatly improved in real-time.

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Zalesky, B. A. "Object tracking algorithm by moving video camera." Doklady of the National Academy of Sciences of Belarus 64, no. 2 (May 17, 2020): 144–49. http://dx.doi.org/10.29235/1561-8323-2020-64-2-144-149.

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The algorithm ACT (Adaptive Color Tracker) to track objects by a moving video camera is presented. One of the features of the algorithm is the adaptation of the feature set of the tracked object to the background of the current frame. At each step, the algorithm extracts from the object features those that are more specific to the object and at the same time are at least specific to the current frame background, since the rest of the object features not only do not contribute to the separation of the tracked object from the background, but also impede its correct detection. The features of the object and background are formed based on the color representations of scenes. They can be computed in two ways. The first way is 3D-color vectors of the clustered image of the object and the background by a fast version of the well-known k-means algorithm. The second way consists in simpler and faster partitioning of the RGB-color space into 3D-parallelepipeds and subsequent replacement of the color of each pixel with the average value of all colors belonging to the same parallelepiped as the pixel color. Another specificity of the algorithm is its simplicity, which allows it to be used on small mobile computers, such as the Jetson TXT1 or TXT2.The algorithm was tested on video sequences captured by various camcorders, as well as by using the well-known TV77 data set, containing 77 different tagged video sequences. The tests have shown the efficiency of the algorithm. On the test images, its accuracy and speed overcome the characteristics of the trackers implemented in the computer vision library OpenCV 4.1.

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CHANG, CHIA-JUNG, JUN-WEI HSIEH, YUNG-SHENG CHEN, and WEN-FONG HU. "TRACKING MULTIPLE MOVING OBJECTS USING A LEVEL-SET METHOD." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 02 (March 2004): 101–25. http://dx.doi.org/10.1142/s0218001404003071.

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This paper presents a novel approach to track multiple moving objects using the level-set method. The proposed method can track different objects no matter if they are rigid, nonrigid, merged, split, with shadows, or without shadows. At the first stage, the paper proposes an edge-based camera compensation technique for dealing with the problem of object tracking when the background is not static. Then, after camera compensation, different moving pixels can be easily extracted through a subtraction technique. Thus, a speed function with three ingredients, i.e. pixel motions, object variances and background variances, can be accordingly defined for guiding the process of object boundary detection. According to the defined speed function, different object boundaries can be efficiently detected and tracked by a curve evolution technique, i.e. the level-set-based method. Once desired objects have been extracted, in order to further understand the video content, this paper takes advantage of a relation table to identify and observe different behaviors of tracked objects. However, the above analysis sometimes fails due to the existence of shadows. To avoid this problem, this paper adopts a technique of Gaussian shadow modeling to remove all unwanted shadows. Experimental results show that the proposed method is much more robust and powerful than other traditional methods.

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Zhong, Qu, Zhang Qingqing, and Gao Tengfei. "Moving Object Tracking Based on Codebook and Particle Filter." Procedia Engineering 29 (2012): 174–78. http://dx.doi.org/10.1016/j.proeng.2011.12.690.

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Jo, Hyeong Geun. "Moving object detection and tracking based on Doppler ultrasound." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4565–69. http://dx.doi.org/10.3397/in-2021-2745.

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Fetal health monitoring during pregnancy has become a necessary procedure. Fetal heart rate (FHR) monitoring can determine fetal development or presence of heart disease and evaluate fetal well-being. The FHR measurement uses typically an acoustic probe-based Doppler ultrasound. Doppler ultrasound method transmits a continuous wave signal to the abdomen of a pregnant woman to receive a reflected signal from the fetal heart. Periodic displacement of the heart tissue produces the Doppler effect and the phase change of the reflected wave is proportional to the velocity of the fetal heart. The reflected signal is modulated into a phase signal and the received signal is demodulated to detect the heart rate. The current clinician system consists of a single probe and requires the probe to be manipulated to the optimal position to measure FHR. The system is highly dependent on trained diagnostic experts. The movement of the pregnant woman and the fetus leads to the misaligned acoustic beam which degrades the reliability of the measurement. This work presents a detection and tracking system using a Doppler signal to compensate for the target's movement. The system is implemented by integrating multi-channel probes interfaced to a Doppler signal converter with a 2-degree of freedom (DOF) motor device. This work describes the characteristics of two key components: Doppler signals of multi-channel probes according to the direction of the acoustic beam and the algorithm with a 2-DOF tracking system.

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Yang, Xiaoyan, and Jinyu Yang. "An improved moving object tracking algorithm based on CAMSHIFT." Journal of Physics: Conference Series 1941, no. 1 (June 1, 2021): 012024. http://dx.doi.org/10.1088/1742-6596/1941/1/012024.

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Ye, Qing, and Yong Mei Zhang. "Moving Object Detection and Tracking Algorithm Based on Background Subtraction." Applied Mechanics and Materials 263-266 (December 2012): 2211–16. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2211.

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Moving target detection and tracking algorithm as the core issue of computer vision and human-computer interaction is the first step of intelligent video surveillance system. Through comparing temporal difference method and background subtraction, a moving object detection and tracking algorithm based on background subtraction under static background is proposed, in order to quickly and accurately detect and identify the moving object in the intelligent monitoring system. In this algorithm, firstly, we use background acquisition method to receive the background image, then use the current frame image and the received background image to perform background subtraction in order to extract foreground object information and receive the difference image; secondly, we use threshold segmentation and morphology image processing to process the difference image in order to eliminate noises and receive the clear binary moving object image; finally, we use the centroid tracking method to track and mark the moving object. Experimental results show that the algorithm can effectively and quickly detect and track moving object from video sequence under static background. This algorithm is easily realized and has good real-time and robust, which is automated and self triggered for background updating. The algorithm can be used in driver assistance systems, motion capture, virtual reality and other fields.

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Wang, Hong, and Jia Deng. "Interacting Multiple Model LK Tracking." Applied Mechanics and Materials 644-650 (September 2014): 1733–36. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1733.

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The nonlinear motion state of object seriously affects the object tracking characteristics in complex motion scene. In this paper, we propose an interacting multiple model LK (IMM-LK) tracking algorithm to enhance the performance of tracking nonlinear moving object. LK tracking approach is based on the localized gradient obtaining stable optical-flow feature, based on LK, we build several motion models of the tracked object that interact with each other in the tracking process. The method extracts different model's object features, estimates the object state and calculates the matching rate of each model with the current motion model using theory of minimum variance. Combining with the optimal transfer matrix then we can track the nonlinear moving object. The proposed IMM-LK algorithm performs favorably against conventional LK tracking on the performance of tracking nonlinear moving object.

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An, Na, and Wei Qi Yan. "Multitarget Tracking Using Siamese Neural Networks." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2s (May 17, 2021): 1–16. http://dx.doi.org/10.1145/3441656.

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In this article, we detect and track visual objects by using Siamese network or twin neural network. The Siamese network is constructed to classify moving objects based on the associations of object detection network and object tracking network, which are thought of as the two branches of the twin neural network. The proposed tracking method was designed for single-target tracking, which implements multitarget tracking by using deep neural networks and object detection. The contributions of this article are stated as follows. First, we implement the proposed method for visual object tracking based on multiclass classification using deep neural networks. Then, we attain multitarget tracking by combining the object detection network and the single-target tracking network. Next, we uplift the tracking performance by fusing the outcomes of the object detection network and object tracking network. Finally, we speculate on the object occlusion problem based on IoU and similarity score, which effectively diminish the influence of this issue in multitarget tracking.

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Zhang, Kaixiang, Jian Chen, and Bingxi Jia. "Asymptotic moving object tracking with trajectory tracking extension: A homography-based approach." International Journal of Robust and Nonlinear Control 27, no. 18 (April 18, 2017): 4664–85. http://dx.doi.org/10.1002/rnc.3823.

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YUN, BYOUNG-JU, JOONG-HOON CHO, and JAE-WOO JEONG. "REAL-TIME OBJECT TRACKING IN MOVING CAMERA." International Journal of Information Acquisition 03, no. 01 (March 2006): 61–67. http://dx.doi.org/10.1142/s0219878906000836.

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Moving object tracking plays an important role in applications of object based video conference, video surveillance and so on. The computational complexity is very important in real-time object tracking. We assumed that the background scene is obtained before an object appears in the image and a camera moves after the object is detected. The proposed method can segment an object by using the difference image if there is no camera motion. After camera motion, it can track the object by using the backward BMA (block matching algorithm) with the HFM (human figure model). For real-time tracking, we used the ROI (region of interest) which is the tightest rectangle of the object. The simulation results show that the proposed method efficiently recognizes and tracks the moving camera as well as the fixed camera.

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Li, Lian, Jun Yi Song, and Zhi Yang Yan. "Moving Object Detection Based on the Fish." Applied Mechanics and Materials 644-650 (September 2014): 1253–56. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1253.

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The detection and tracking of moving object is the important research of image analysis and understanding as well as in computer vision field, and have extensive application in the traffic monitoring, the military, industrial process control and medical research, but less application in the underwater monitoring of fish. In this paper, in order to be able to real-time detection of the fish in the digital video system moving target, proposed the fish moving target detection algorithm under a camera. With an improved background updating method of adaptive Gaussian mixture model, a method to detect the target fish based on Gaussian mixture model combined with edge detection operator.

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R, Amith, and V. N. Manjunath Aradhya. "Object Detection and Tracking in Discriminant Subspace." IRA-International Journal of Technology & Engineering (ISSN 2455-4480) 7, no. 3 (July 5, 2017): 62. http://dx.doi.org/10.21013/jte.v7.n3.p2.

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<div><p>Detection and tracking of moving objects in video is essential for many computer vision applications and it is considered as a challenging research issue due to dynamic changes in background, illumination, object size and shape. Many traditional algorithms fails to detect and track the moving objects accurately, this paper proposes a robust method, to detect and track moving objects based on the combination of background subtraction and Orthogonalized Fisher’s Discriminant (OFD). Background subtraction detects the foreground objects on subtracting frame by frame basis and updating the background model recursively. Orthogonalized Fisher’s Discriminant projects high dimensional data onto a one dimensional space with the highest recognizability, which speedup the detection and tracking process and also preserves the structure of the objects resulting high accuracy. The proposed method is tested on standard datasets with complex environments and experimental results obtained are encouraging.</p></div>

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Yang, Qi, and Jia Fu Jiang. "PFCHA: A New Moving Object Tracking Algorithms Based on Particle Filter and Histogram." Applied Mechanics and Materials 110-116 (October 2011): 3343–50. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.3343.

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The complexity of the video background of moving target tracking algorithm led to the robustness of the important reasons is not high for the limitations of existing algorithms, a framework based on the movement of particle filter tracking algorithm. In order to reduce the impact of occlusion for the algorithm, the algorithm of moving objects make full use of color and motion characteristics of moving target detection, and to avoid the interference of the complex background, within the framework of particle filter in the object color histogram analysis. Finally, given an effective comparison of the calculation. Experimental results show that particle filter based target tracking algorithm can effectively remove the interference of the complex background, the context for any trace detection of high robustness.

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Han, Zhe Xin, and Bo Hu Zhang. "Research of Tracking Algorithm for Moving Object Based on Video Sequence." Applied Mechanics and Materials 556-562 (May 2014): 3088–92. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3088.

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The study of tracking algorithm of moving objects is a key technique in computer vision; therefore, the work done by this thesis has important theoretical significance and wide practical value. An algorithm based on the improved three-frame-differencing is introduced in this paper. Firstly, three continuous edge images are obtained by edge extract from three continuous images, then, the motion information is detected with three-frame-differencing, finally, the target is extracted by threshold segmentation and morphology. The method is simple in calculation. The experimental results show that the strong robustness and can accurately detect the moving target. In addition, using the algorithm based on the characteristics of the objects can clearly record the object’s trajectory. Experiments show that the algorithm is effective and feasible in the motion object detection.

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Naushad Ali, M. M., M. Abdullah-Al-Wadud, and Seok Lyong Lee. "Moving Object Detection and Tracking Using Particle Filter." Applied Mechanics and Materials 321-324 (June 2013): 1200–1204. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1200.

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Moving human detection and tracking are challenging tasks in computer vision. Human motion is usually non-linear and non-Gaussian, and thus many common algorithms are not appropriate for tracking. In this paper we propose a robust tracking algorithm based on particle filter. Multiple moving human in a video sequence are detected using frame difference and morphological operation. Then feature points of every person are extracted using a Harris Corner detection algorithm. Finally, Histogram of Oriented Gradient (HOG) is calculated for each feature point and feature points of the corresponding person are tracked using particle filter. Experimental results demonstrate that our method is efficient to improve the performance of tracking.

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Zhou, Zhong Wei, Min Xu, Wei Fu, and Ji Zeng Zhao. "Object Tracking and Positioning Based on Stereo Vision." Applied Mechanics and Materials 303-306 (February 2013): 313–17. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.313.

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The goal of this paper is to present a method for object tracking and positioning based on stereo vision in real time. The method effectively combined stereo matching algorithm with object tracking algorithm, and calculated the spatial location information of the object by using binocular stereo vision while the object is being tracked. The stereo matching used dynamic programming, image pyramids and control points modification algorithm, and the object tracking mainly utilized CamShift algorithm in this paper. The experimental results have confirmed that the proposed method realized real-time tracking for moving object, accurate calculating for the object three-dimensional coordinates, which meet the applied needs of servo follow-up system.

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Chu, Jing Hui, Xing Pei Zhai, Guan Nan Su, and Cai Lian Chen. "Real-Time Object Tracking Based on Android Platform." Advanced Materials Research 403-408 (November 2011): 1438–41. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1438.

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A real-time application is proposed for Android smart phone. To achieve the best tradeoff between the complexity and the processing efficiency, Java Native Interface (JNI) is used rather than pure Java. The performance of the application is tested on an Android smart phone by dealing with various pedestrian videos. And the experimental results show that the application successfully detects and tracks certain moving object.

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Sun, Zhi Hai, Bin Hu, Ying Meng, and Wen Hui Zhou. "Tracking of Moving Objects in Video Sequences Based on Elliptical Subtractive Clustering." Applied Mechanics and Materials 734 (February 2015): 600–603. http://dx.doi.org/10.4028/www.scientific.net/amm.734.600.

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Visual object detection and tracking have become an important step between computer vision and video analysis. Recent methods almost use mean shift for tracking problems, which are difficult to overcome the shortcoming with the initial object model. Initial object model is almost initialized manually by user, which is not smart enough and inconvenient. This paper considers the integration strategy for elliptical subtractive clustering and mean shift, and proposes a novel tracking method based on elliptical subtractive clustering.

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Żak, Bogdan, and Stanisław Hożyń. "Moving Object Detection, Localization and Tracking Using Stereo Vison System." Solid State Phenomena 236 (July 2015): 134–41. http://dx.doi.org/10.4028/www.scientific.net/ssp.236.134.

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The aim of this study was to design an moving object detection, localization and tracking algorithm able to detect, localize and track especially humans and vehicles. We focused on triangulation techniques to calculate the position of the detected objects in a stereo vision rig coordinates frame. For objects detection and tracking the novel algorithm, based on statistical image processing methods, was proposed. Verification of a proper operation of the elaborated method was made by conducting series of experiments. Our results indicate that the algorithm localizes, detects and tracks objects accurately for the most tested conditions.

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Journal articles on the topic 'Rule-Based Moving Object Tracking'

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