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A multistate pedestrian target recognition and tracking algorithm in public places based on camshift algorithm

INTERNATIONAL JOURNAL OF BIOMETRICS 2024年第5期16卷 431-448页

作者： Han, GaoFeng Zhong, Yuanquan Anhui Wenda Univ Informat Engn Hefei 231201 Peoples R China

In order to improve the accuracy of polymorphic pedestrian target recognition and tracking, and shorten tracking time, this paper proposes a public place polymorphic pedestrian target recognition and tracking algorithm based on the camshift algorithm. Firstly, greyscale the input image and use Hog to select polymorphic pedestrian target features in public places. Then, calculate the probability density of the target area model and construct a pedestrian target recognition and tracking model. Finally, extract the colour features of the target, select the Bhattacharyya coefficient to calculate the similarity between the target model and the candidate model, and use the camshift algorithm for target recognition, tracking, and matching to obtain the final recognition and tracking results. The experimental results show that the accuracy of the proposed method can reach 97.78 and the operation time is only 0.082 frames/s, indicating that the proposed method effectively improves the target recognition and tracking performance.

关键词： gamma correction method camshift algorithm HSV space hue histogram search window

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Improved camshift algorithm in AGV Vision-based Tracking with Edge Computing

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JOURNAL OF SUPERCOMPUTING 2022年第2期78卷 2709-2723页

作者： Zhang, Tongpo Nie, Xiaokai Zhu, Xu Lim, Enggee Ma, Fei Yu, Limin Xian Jiaotong Liverpool Univ Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England

Automated guided vehicles (AGVs) are Internet of Things robots that navigate automatically as guided by a central control platform with distributed intelligence. Different methodologies have been proposed for AGV visual tracking applications. However, vision-based tracking in AGVs usually confronts the problem of time delay caused by the complexity of image processing algorithms. To balance the trade-off among algorithm complexity, hardware cost and performance, precision and robustness are usually compromised in practical deployment. This paper proposes a prototype design of a visual tracking system. Edge computing is implemented which migrates computation intensive image processing to a local computer. The Raspberry Pi-based AGV captures the real-time image through the camera, sends the images to the computer and receives the processing results through the WiFi link. An improved camshift algorithm is developed and implemented. Based on this algorithm, the AGV can make convergent prediction of the pixels in the target area after the first detection of the object. Relative coordinates of the target can be located more accurately in less time. As tested in the experiments, the system architecture and new algorithm lead to reduced hardware cost, less time delay, improved robustness and higher accuracy in tracking.

关键词： camshift algorithm AGV Visual tracking Edge computing

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Research on camshift algorithm Based on Feature Matching and Prediction Mechanism 18

Research on CAMshift Algorithm Based on Feature Matching and...

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IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Hui Zhang, Quchang Yu, Lijun Wang, Zhiqiang Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781665441018

Aiming at the problem of target loss caused by background interference and occlusion in the traditional camshift algorithm for target tracking, a camshift tracking algorithm based on feature matching and prediction mechanism is designed. The algorithm re-locates the size and position of the target by ORB feature matching between the template target and the frame to be tracked, and realizes the accurate tracking of the target under the interference of similar background and complex background. In order to solve the problem of inaccurate tracking and feature matching under occlusion, the Kalman filter is used to predict the position of the occluded target. The experimental results show that the algorithm can accurately track the target under complex background, similar background and occlusion.

关键词： Target Tracking camshift algorithm ORB algorithm Kalman Filter

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High-speed vision extraction based on the camshift algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 555-564页

作者： Zhang, ChunYu Chen, Lei Gou, Rui Bin Univ Sci & Technol Anhui Coll Mech Engn Fengyang 233100 Peoples R China Anhui Univ Technol Coll Mech Engn Maanshan 243000 Peoples R China

This study investigates the use of the continuously adaptive mean shift (camshift) algorithm in high-speed vision extraction. Videos of a high-speed target are captured with a high-speed camera. The displacement of the target is extracted from the videos using the camshift algorithm. The camshift algorithm is then compared with the normalized cross correlation algorithm and other algorithms in terms of accuracy and rapidity. Simulation and test results indicate that the camshift algorithm is better than other algorithms in terms of the displacement extraction of high-frequency vibration. The camshift algorithm also offers numerous advantages, including real time, high efficiency, high accuracy, and robustness.

关键词： camshift algorithm Displacement extraction High-speed vision NCC algorithm Color histogram

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Moving target location method based on Euclidean distance and camshift algorithm 8

Moving target location method based on Euclidean distance an...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Liu, Mingzhu Chen, Huixian Qiu, Zhiqiang Ren, Xiangxiang Harbin Univ Sci & Technol Sch Measurement & Control Technol & Commun Engn Harbin Peoples R China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682463

Moving target location and tracking is a very complicated problem, especially, the occlusion problem gradually becomes the limitation of the practical tracking algorithm. Because of the moving targets in video monitoring can be occluded by the obstacles in the moving process, it can reduce target positioning accuracy, and even lead to the target track losing. To resolve the problems, a new location algorithm has been proposed in this paper. The preprocessing of video image is realized through the basic process of Gauss foreground detection, Kalman filter, expansion and occlusion judgment. And then, the method based on the Kalman prediction and the adaptive threshold value which determined by the Euclidean distance of the target motion trajectory, and the target centroids have been searched by camshift algorithm. The test results show that the method of using the Euclidean distance of the target trajectory as the prediction threshold can not only effectively locate the moving target, but also resist to the adverse effects of the occlusion object, and reduce the loss rate of tracking.

关键词： target positioning camshift algorithm threshold value Euclidean distance

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Visible Light Dynamic Positioning Method Using Improved camshift-Kalman algorithm

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2019年第6期11卷

作者： Guan, Weipeng Liu, Zhipeng Wen, Shangsheng Xie, Hongyun Zhang, Xingjie South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Shenzhen South Tech Informat Technol Co Ltd Shenzhen Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Mat Sci & Engn Guangzhou 510640 Peoples R China China Nucl Power Engn Co Ltd State Key Lab Nucl Power Safety Monitoring Techno Shenzhen 518172 Peoples R China

There are three critical elements in the visible light positioning (VLP) system: Positioning accuracy, real-time ability and robustness. However, few existing VLP studies consider these three critical elements at the same time. Especially, robustness is usually ignored in VLP system, which has a great influence on positioning performance or even leads to the failure of positioning. Therefore, we propose a novel VLP method based on image sensor (as positioning terminal), using improved camshift-Kalman algorithm. The proposed algorithm not only combines camshift algorithm with Kalman filter, but also introduces the Bhattacharyya coefficient innovatively. It can realize high positioning accuracy, good robustness and good real-time ability. Experiments showed that the positioning accuracy of our proposed VLP algorithm was 0.55 cm, which realized high positioning accuracy. Besides, the average processing time per frame was 27.2 ms, which realized good real-time ability. Also, whether the LED was shielded, the interference occurred or the target's velocity changed suddenly, the proposed algorithm still maintained good positioning effect, which showed that it had good robustness.

关键词： Visible light positioning (VLP) real-time ability robustness image sensor (IS) camshift algorithm Kalman filter Bhattacharyya coefficient

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A real time target face tracking algorithm based on saliency detection and camshift

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第28期82卷 43599-43624页

作者： Wang, Junnan Jia, Zhenhong Lai, Huicheng Shi, Fei Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Peoples R China

To take advantage of the speed advantage of the camshift and try to overcome the problem of its poor robustness in target tracking, in this paper, a real time target face tracking algorithm based on saliency detection and camshift is proposed. Considering that the target to be tracked is more significant than the background in the frame, the saliency detection algorithm MBplus is first used to remove the background around the target as much as possible, so as to reduce the interference caused by the background to the camshift tracking results. Then the camshift is used to search and localize the targets in the processed video frames. At the same time, to compensate for the lack of tracking ability of camshift for some characteristic targets, the Kalman filter is used to predict the position of the target in the current frame. Finally, the Kalman-predicted target position, the target position obtained by camshift, are compared with the target tracked in the previous frame, and the position with high similarity is considered as the target tracking result of this paper. The experimental results show that the average tracking precision of the proposed target face tracking algorithm on the Birchfield database is 94.0%, its average tracking success rate on the NRC-IIT Facial Video Database is 100%, and even for the target faces with few attributes in ytcelebrity database, its tracking precision and tracking success rate are all 100%, which are superior to some state-of-the-art tracking algorithms.

关键词： Saliency detection Kalman filter camshift algorithm Face tracking

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An Improved camshift algorithm for Target Recognition 9

An Improved Camshift Algorithm for Target Recognition

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9th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Automatic Target Recognition and Navigation

作者： Fu Min Cai Chao Mao Yusu Huazhong Univ Sci & Technol Sch Automat State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China Wuhan Inst Technol Coll Sci Wuhan Peoples R China

ISBN: (纸本)9781510600539

camshift algorithm and three frame difference algorithm are the popular target recognition and tracking methods. camshift algorithm requires a manual initialization of the search window, which needs the subjective error and coherence, and only in the initialization calculating a color histogram, so the color probability model cannot be updated continuously. On the other hand, three frame difference method does not require manual initialization search window, it can make full use of the motion information of the target only to determine the range of motion. But it is unable to determine the contours of the object, and can not make use of the color information of the target object. Therefore, the improved camshift algorithm is proposed to overcome the disadvantages of the original algorithm, the three frame difference operation is combined with the object's motion information and color information to identify the target object. The improved camshift algorithm is realized and shows better performance in the recognition and tracking of the target.

关键词： color probability model camshift algorithm frame difference target detection

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Improved camshift object tracking algorithm in occluded scenes based on AKAZE and Kalman

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第2期81卷 2145-2159页

作者： Pei, Lili Zhang, He Yang, Bo Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China Xian Xiangteng Microelect Technol Co Ltd Xian 710068 Shaanxi Peoples R China Datang Mobile Commun Equipment Co Ltd Xian Branch Xian 710061 Shaanxi Peoples R China

camshift algorithm tracking is susceptible to interference when a tracking object is occluded or when its hue is similar to the background. An improved camshift object-tracking algorithm combining AKAZE (Accelerated-KAZE) feature matching and Kalman filtering is proposed. First, the video channel is converted for processing. Second, AKAZE is used to match the object feature points and Kalman filtering is used to predict the next position. Then different scenes are judged by the threshold and the camshift and Kalman tracking algorithms are used for object tracking, respectively. Finally, the improved camshift algorithm is used to test the moving object in a variety of situations and compared with the traditional camshift algorithm and the Kalman filter improved camshift algorithm. Experimental results show that the improved joint tracking algorithm can continue tracking under full occlusion. The effective frame rate of recognition is increased by about 20%, and the single-frame image processing time is less than 35 ms, which can meet the real-time tracking requirements.

关键词： AKAZE algorithm camshift algorithm Feature matching Kalman filtering Object tracking Video processing

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Detection and Tracking of Human Motion Targets in Video Images Based on camshift algorithms

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IEEE SENSORS JOURNAL 2020年第20期20卷 11887-11893页

作者： Zhang, Yin Zhengzhou Vocat Coll Finance & Taxat Zhengzhou 450000 Peoples R China

For the traditional moving target tracking algorithm, it is difficult to detect the target area accurately because of the influence of environmental factors. Therefore, manual selection of moving targets is often used, but this method is not intelligent enough, and has strong subjectivity and experience, so the data obtained is not convincing enough. For this reason, based on the multi-frame subtraction method, the connected region search is used to select the appropriate moving target and calibrate the target area, and then the moving target is determined and tracked. At the target tracking stage, the fusion feature vectors in the calibrated area are updated in real time to improve the accuracy and robustness of the follow-up tracking algorithm. Experiments show that the method is feasible. In the stage of target detection, the improved algorithm filters and calibrates the target area on the premise of increasing a small amount of computation. In the stage of target tracking, the computational area is reduced to the calibration and tracking range, which improves the efficiency of the algorithm and guarantees the stability and robustness of the algorithm.

关键词： Target tracking Image color analysis Heuristic algorithms Object detection Robustness Analytical models Target detection multi-frame subtraction camshift algorithm target tracking

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