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.
@article{arxiv.2507.12832,
title = {MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results},
author = {Yuki Kondo and Norimichi Ukita and Riku Kanayama and Yuki Yoshida and Takayuki Yamaguchi and Xiang Yu and Guang Liang and Xinyao Liu and Guan-Zhang Wang and Wei-Ta Chu and Bing-Cheng Chuang and Jia-Hua Lee and Pin-Tseng Kuo and I-Hsuan Chu and Yi-Shein Hsiao and Cheng-Han Wu and Po-Yi Wu and Jui-Chien Tsou and Hsuan-Chi Liu and Chun-Yi Lee and Yuan-Fu Yang and Kosuke Shigematsu and Asuka Shin and Ba Tran},
journal= {arXiv preprint arXiv:2507.12832},
year = {2025}
}
Comments
This paper is the official challenge report for SMOT4SB and is published in the proceedings of MVA 2025 (19th International Conference on Machine Vision and Applications). Official challenge page: https://www.mva-org.jp/mva2025/challenge