Weiran Li — accepted AI-conference papers

2026

When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking

Weiran Li, Yeqiang Liu, Qiannan Guo, Yijie Wei, Hwa Liang Leo, Zhenbo Li

AAAI 2026technical

Multiple object tracking (MOT) technology has made significant progress in terrestrial applications, but underwater tracking scenarios remain underexplored despite their importance to marine ecology and aquaculture. In this paper, we present Multiple Fish Tracking Dataset 2025 (MFT25), a comprehensi

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2025

PDTrack: Progressive Distance Association for Multiple Object Tracking

Yeqiang Liu, Weiran Li, Yanhao Ding, Zhenbo Li

ICASSP 2025accepted

Multiple Object Tracking (MOT) has made significant progress in recent years. However, it still faces challenges such as frequent ID switches, trajectory fragmentation, and tracking losses in high-density pedestrian scenarios. To address these issues, we optimized the association algorithm based on…

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