MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results

Yuki Kondo; Norimichi Ukita; Riku Kanayama; Yuki Yoshida; Takayuki Yamaguchi; Xiang Yu; Guang Liang; Xinyao Liu; Guan-Zhang Wang; Wei-Ta Chu; Bing-Cheng Chuang; Jia-Hua Lee; Pin-Tseng Kuo; I-Hsuan Chu; Yi-Shein Hsiao; Cheng-Han Wu; Po-Yi Wu; Jui-Chien Tsou; Hsuan-Chi Liu; Chun-Yi Lee; Yuan-Fu Yang; Kosuke Shigematsu; Asuka Shin; Ba Tran

arXiv · 2507.12832

MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results

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Abstract

Small Multi-Object Tracking (SMOT) is particularly challenging when targets occupy only a few dozen pixels, rendering detection and appearance-based association unreliable. Building on the success of the MVA2023 SOD4SB challenge, this paper introduces the SMOT4SB challenge, which leverages temporal information to address limitations of single-frame detection. Our three main contributions are: (1) the SMOT4SB dataset, consisting of 211 UAV video sequences with 108,192 annotated frames under diverse real-world conditions, designed to capture motion entanglement where both camera and targets move freely in 3D; (2) SO-HOTA, a novel metric combining Dot Distance with HOTA to mitigate the sensitivity of IoU-based metrics to small displacements; and (3) a competitive MVA2025 challenge with 78 participants and 308 submissions, where the winning method achieved a 5.1x improvement over the baseline. This work lays a foundation for advancing SMOT in UAV scenarios with applications in bird strike avoidance, agriculture, fisheries, and ecological monitoring.

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Yuki Kondo, Norimichi Ukita, Riku Kanayama, Yuki Yoshida, Takayuki Yamaguchi, Xiang Yu, Guang Liang, Xinyao Liu, Guan-Zhang Wang, Wei-Ta Chu, Bing-Cheng Chuang, Jia-Hua Lee, Pin-Tseng Kuo, I-Hsuan Chu, Yi-Shein Hsiao, Cheng-Han Wu, Po-Yi Wu, Jui-Chien Tsou, Hsuan-Chi Liu, Chun-Yi Lee, Yuan-Fu Yang, Kosuke Shigematsu, Asuka Shin, Ba Tran. 2025-07-17. MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results. https://arxiv.org/abs/2507.12832

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