Tracklet Generation Method by Submodular Optimization for Multi-Object Tracking

SUN Jin; DU Guanming

doi:10.11999/JEIT230208

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 995-1004

SUN Jin, DU Guanming. Tracklet Generation Method by Submodular Optimization for Multi-Object Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(3): 995-1004. doi: 10.11999/JEIT230208

Citation:

SUN Jin, DU Guanming. Tracklet Generation Method by Submodular Optimization for Multi-Object Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(3): 995-1004. doi: 10.11999/JEIT230208

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Tracklet Generation Method by Submodular Optimization for Multi-Object Tracking

doi: 10.11999/JEIT230208

SUN Jin^,,
DU Guanming

College of Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (61702260)

Received Date: 2023-03-30
Rev Recd Date: 2023-11-06

Available Online: 2023-11-14

Publish Date: 2024-03-27

Abstract

Abstract

As the basis of many intelligent visual tasks, Multi-Object Tracking (MOT) is a challenging problem in computer vision. Occlusion is a main factor affecting the tracking accuracy. To solve the occlusion problem, in this paper, the strategy of tracking-by-detection is adopted to obtain complete trajectories of targets based on associating tracklets. Meanwhile, to improve the tracking robustness, the tracklet generation problem is transformed into the facility location problem in operations research area and further a submodular optimization based tracklet generation method is proposed. In this method, two complementary features including Histogram of Oriented Gradient (HOG)and Color Name (CN) are integrated to describe the target appearance, and a weighting coefficient is also designed by motion information to improve the matching accuracy. At length, a submodular maximization algorithm with constraints is developed to achieve the global data association by selecting the targets to form the tracklets. By comparative experiments on the benchmark datasets, the proposed method can solve the occlusion problem effectively with guaranteed performance.
- Multi-Object Tracking (MOT),
- Tracklet,
- Data association,
- Submodular optimization

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References(37)

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