追踪10条群游鳟鱼的运动轨迹，助力动物行为研究
Fish Tracking Challenge 2024: A Multi-Object Tracking Competition with Sweetfish Schooling Data
- 基于视频与框选标注，构建多目标追踪任务
- 10条鳟鱼在复杂环境中实现高精度轨迹追踪
- 适合动物行为、计算机视觉领域研究人员
动物集体行为研究，尤其是在水生环境中，为行为学、生态学和生物导航领域带来了独特的挑战与机遇。Fish Tracking Challenge 2024（https://ftc-2024.github.io/）引入一项聚焦于群游鳟鱼复杂行为的多目标追踪竞赛。使用SweetFish数据集，参赛者需开发先进追踪模型，准确监测10条鳟鱼的实时位置。本文介绍了竞赛背景、目标、SweetFish数据集，以及前三名及基准方法的策略。通过视频数据与边界框标注，竞赛旨在推动自动检测与追踪算法的创新，应对水生动物运动的复杂性。该挑战凸显了多目标追踪在揭示集体动物行为动态中的重要性，有望显著推进上述领域的科学认知。
原文摘要 · Abstract (English)
The study of collective animal behavior, especially in aquatic environments, presents unique challenges and opportunities for understanding movement and interaction patterns in the field of ethology, ecology, and bio-navigation. The Fish Tracking Challenge 2024 (https://ftc-2024.github.io/) introduces a multi-object tracking competition focused on the intricate behaviors of schooling sweetfish. Using the SweetFish dataset, participants are tasked with developing advanced tracking models to accurately monitor the locations of 10 sweetfishes simultaneously. This paper introduces the competition's background, objectives, the SweetFish dataset, and the appraoches of the 1st to 3rd winners and our baseline. By leveraging video data and bounding box annotations, the competition aims to foster innovation in automatic detection and tracking algorithms, addressing the complexities of aquatic animal movements. The challenge provides the importance of multi-object tracking for discovering the dynamics of collective animal behavior, with the potential to significantly advance scientific understanding in the above fields.
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