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Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

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Pubblicato in:Sensors vol. 24, no. 3 (2024), p. 779
Autore principale: Huang, Cheng
Altri autori: Li, Weihong, Yang, Guang, Yan, Jiachen, Zhou, Baoding, Li, Yujun
Pubblicazione:
MDPI AG
Soggetti:
Accuracy
Cameras
Algorithms
Security management
Surveillance
Localization
Video recordings
Pedestrians
Calibration
Neural networks
Accesso online:Citation/Abstract
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024 7 |a 10.3390/s24030779  |2 doi 
035 |a 2924004598 
045 2 |b d20240101  |b d20241231 
084 |a 231630  |2 nlm 
100 1 |a Huang, Cheng  |u School of Geography, South China Normal University, Guangzhou 510631, China; <email>huangch@m.scnu.edu.cn</email> (C.H.); <email>liweihong@m.scnu.edu.cn</email> (W.L.); <email>2021023461@m.scnu.edu.cn</email> (J.Y.); <email>2023023398@m.scnu.edu.cn</email> (Y.L.); Guangdong Shida Weizhi Information Technology Co., Ltd., Qingyuan 511500, China 
245 1 |a Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint 
260 |b MDPI AG  |c 2024 
513 |a Journal Article 
520 3 |a Pedestrian tracking in surveillance videos is crucial and challenging for precise personnel management. Due to the limited coverage of a single video, the integration of multiple surveillance videos is necessary in practical applications. In the realm of pedestrian management using multiple surveillance videos, continuous pedestrian tracking is quite important. However, prevailing cross-video pedestrian matching methods mainly rely on the appearance features of pedestrians, resulting in low matching accuracy and poor tracking robustness. To address these shortcomings, this paper presents a cross-video pedestrian tracking algorithm, which introduces spatial information. The proposed algorithm introduces the coordinate features of pedestrians in different videos and a linear weighting strategy focusing on the overlapping view of the tracking process. The experimental results show that, compared to traditional methods, the method in this paper improves the success rate of target pedestrian matching and enhances the robustness of continuous pedestrian tracking. This study provides a viable reference for pedestrian tracking and crowd management in video applications. 
653 |a Accuracy 
653 |a Cameras 
653 |a Algorithms 
653 |a Security management 
653 |a Surveillance 
653 |a Localization 
653 |a Video recordings 
653 |a Pedestrians 
653 |a Calibration 
653 |a Neural networks 
700 1 |a Li, Weihong  |u School of Geography, South China Normal University, Guangzhou 510631, China; <email>huangch@m.scnu.edu.cn</email> (C.H.); <email>liweihong@m.scnu.edu.cn</email> (W.L.); <email>2021023461@m.scnu.edu.cn</email> (J.Y.); <email>2023023398@m.scnu.edu.cn</email> (Y.L.); Guangdong Shida Weizhi Information Technology Co., Ltd., Qingyuan 511500, China; SCNU Qingyuan Institute of Science and Technology Innovation, Qingyuan 511500, China 
700 1 |a Yang, Guang  |u School of Geography, South China Normal University, Guangzhou 510631, China; <email>huangch@m.scnu.edu.cn</email> (C.H.); <email>liweihong@m.scnu.edu.cn</email> (W.L.); <email>2021023461@m.scnu.edu.cn</email> (J.Y.); <email>2023023398@m.scnu.edu.cn</email> (Y.L.); Guangdong Shida Weizhi Information Technology Co., Ltd., Qingyuan 511500, China; SCNU Qingyuan Institute of Science and Technology Innovation, Qingyuan 511500, China 
700 1 |a Yan, Jiachen  |u School of Geography, South China Normal University, Guangzhou 510631, China; <email>huangch@m.scnu.edu.cn</email> (C.H.); <email>liweihong@m.scnu.edu.cn</email> (W.L.); <email>2021023461@m.scnu.edu.cn</email> (J.Y.); <email>2023023398@m.scnu.edu.cn</email> (Y.L.); Guangdong Shida Weizhi Information Technology Co., Ltd., Qingyuan 511500, China 
700 1 |a Zhou, Baoding  |u College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China; <email>bdzhou@szu.edu.cn</email> 
700 1 |a Li, Yujun  |u School of Geography, South China Normal University, Guangzhou 510631, China; <email>huangch@m.scnu.edu.cn</email> (C.H.); <email>liweihong@m.scnu.edu.cn</email> (W.L.); <email>2021023461@m.scnu.edu.cn</email> (J.Y.); <email>2023023398@m.scnu.edu.cn</email> (Y.L.) 
773 0 |t Sensors  |g vol. 24, no. 3 (2024), p. 779 
786 0 |d ProQuest  |t Health & Medical Collection 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2924004598/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/2924004598/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/2924004598/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch