Smart City Gnosys

Smart city article details

Title	Multi-Camera Tracking By Candidate Intersection Ratio Tracklet Matching
ID_Doc	38146
Authors	Li Y.-L.; Chin Z.-Y.; Chang M.-C.; Chiang C.-K.
Year	2021
Published	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
DOI	http://dx.doi.org/10.1109/CVPRW53098.2021.00463
Abstract	Multi-camera vehicle tracking at the city scale is an essential task in traffic management for smart cities. Large-scale video analytics is challenging due to the vehicle variabilities, view variations, frequent occlusions, degraded pixel quality, and appearance differences. In this work, we develop a multi-target multi-camera (MTMC) vehicle tracking system based on a newly proposed Candidates Intersection Ratio (CIR) metric that can effectively evaluate vehicle tracklets for matching across views. Our system consists of four modules: (1) Faster-RCNN vehicle detection, (2) detection association based on re-identification feature matching, (3) single-camera tracking (SCT) to produce initial tracklets, (4) multi-camera vehicle tracklet matching and re-identification that creates longer, consistent tracklets across the city scale. Based on popular DNN object detection and SCT modules, we focus on the development of tracklet creation, association, and linking in SCT and MTMC. Specifically, SCT filters are proposed to effectively eliminate unreliable tracklets. The CIR metric improves robust vehicle tracklet linking across visually distinct views. Our system obtains IDF1 score of 0.1343 on the AI City 2021 Challenge Track 3 public leaderboard. © 2021 IEEE.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
38147	0.915	Liu Y.; Zhang X.; Zhang B.; Zhang X.; Wang S.; Xu J.	Multi-Camera Vehicle Tracking Based On Occlusion-Aware And Inter-Vehicle Information	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2022-June (2022)
58684	0.883	Hsu H.-M.; Wang Y.; Hwang J.-N.	Traffic-Aware Multi-Camera Tracking Of Vehicles Based On Reid And Camera Link Model	MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia (2020)
22582	0.873	Qian Y.; Yu L.; Liu W.; Hauptmann A.G.	Electricity: An Efficient Multi-Camera Vehicle Tracking System For Intelligent City	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June (2020)
54231	0.873	Herzog F.; Chen J.; Teepe T.; Gilg J.; Hormann S.; Rigoll G.	Synthehicle: Multi-Vehicle Multi-Camera Tracking In Virtual Cities	Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 (2023)
16613	0.871	Li B.; Qin K.; Cui Z.; Xu Q.; Xu Z.	Cross-Camera Vehicle Trajectory Estimation Towards Traffic Flow	2022 International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2022 (2022)
5878	0.871	Lin Y.; Lockyer S.; Evans A.; Zarbock M.; Zhang N.	Ablation Study For Multi-Camera Vehicle Tracking Using Cityflow Dataset	Proceedings of SPIE - The International Society for Optical Engineering, 13517 (2025)
23868	0.869	Tseng Y.-S.; Su Y.-F.; Lin D.-T.	Enhancing Multi-Target Multi-Camera Vehicle Tracking With Yolov9 And Attention Mechanisms For Smart City Traffic Monitoring	Multimedia Tools and Applications (2025)
38433	0.864	Hsu H.-M.; Wang Y.; Cai J.; Hwang J.-N.	Multi-Target Multi-Camera Tracking Of Vehicles By Graph Auto-Encoder And Self-Supervised Camera Link Model	Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2022 (2022)
23882	0.864	Ahmed M.; El-Sheimy N.; Leung H.	Enhancing Object Tracking In Smart City Intelligent Transportation Systems: A Track-By-Detection Approach Utilizing Satellite Video Monitoring	2024 IEEE International Conference on Smart Mobility, SM 2024 (2024)
17814	0.859	Wang X.; Sun Z.; Chehri A.; Jeon G.; Song Y.	Deep Learning And Multi-Modal Fusion For Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, And Comparative Study	Information Fusion, 105 (2024)