TPM: Multiple object tracking with tracklet-plane matching


Peng, Jinlong and Wang, Tao and Lin, Weiyao and Wang, Jian and See, John and Wen, Shilei and Ding, Erui (2020) TPM: Multiple object tracking with tracklet-plane matching. Pattern Recognition, 107. p. 107480. ISSN 0031-3203

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Multiple object tracking (MOT) aims to model the temporal relationship among detected objects and associate them into trajectories. Thus, one major challenge of MOT lies in the confusion from noisy object detection results. In this paper, we propose Tracklet-Plane Matching (TPM), a new approach which improves the performance of MOT by modeling and reducing the interferences from noisy or confusing object detections. TPM first constructs good temporally-related object detections into short tracklets. Then, a tracklet-plane matching process is introduced to organize related tracklets into planes and associate them into long trajectories. The tracklet-plane matching process assigns visually confusing tracklets into different tracklet planes according to their contextual information, thus properly reducing the confusion among similar tracklets. At the same time, it also allows association among temporally non-neighboring or overlapping tracklets, which provides good flexibility to handle confusion from noisy detections. Under this process, a tracklet-importance evaluation scheme and a representative-based similarity modeling scheme are introduced. These two schemes can properly evaluate the reliability of detection results and identify reliable ones during association so that the impact of noisy or confusing detections can be well-mitigated. Experimental results on benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art MOT methods.

Item Type: Article
Uncontrolled Keywords: Object monitors (Computer software), Multiple object tracking, Tracklet, Tracklet-plane, Representative-selection network
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 14 Dec 2020 10:09
Last Modified: 14 Dec 2020 10:09


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