为水下鱼群追踪打造新数据集与专用跟踪框架
When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking
- 用改进的无迹卡尔曼滤波建模鱼的非线性游动轨迹
- 在15个视频上实现34.1的HOTA和44.6的IDF1
- 专为水下鱼群设计，适合海洋生态与养殖研究者
多目标跟踪技术在陆地应用中已取得显著进展，但水下场景仍研究不足，而其对海洋生态与水产养殖至关重要。本文提出针对水下多鱼跟踪的多重鱼追踪数据集2025（MFT25），包含15个多样视频序列，覆盖48,066帧，共408,578个精细标注边界框，涵盖多种水下环境、鱼种及遮挡、外观相似、运动模式混乱等挑战性条件。同时提出尺度感知与无迹跟踪器（SU-T），采用优化的无迹卡尔曼滤波（UKF）处理鱼类非线性游动，并设计新型鱼形交并比（FishIoU）匹配机制，考虑水生物种形态特征。大量实验表明，该框架在MFT25上达到34.1 HOTA和44.6 IDF1，处于领先水平，揭示了鱼类跟踪与陆地目标跟踪的根本差异。数据集与代码已公开于https://vranlee.github.io/SU-T/。
原文摘要 · Abstract (English)
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 comprehensive dataset specifically designed for underwater multiple fish tracking, featuring 15 diverse video sequences with 408,578 meticulously annotated bounding boxes across 48,066 frames. Our dataset captures various underwater environments, fish species, and challenging conditions including occlusions, similar appearances, and erratic motion patterns. Additionally, we introduce Scale-aware and Unscented Tracker (SU-T), a specialized tracking framework featuring an Unscented Kalman Filter (UKF) optimized for non-linear swimming patterns of fish and a novel Fish-Intersection-over-Union (FishIoU) matching that accounts for the unique morphological characteristics of aquatic species. Extensive experiments demonstrate that our SU-T baseline achieves state-of-the-art performance on MFT25, with 34.1 HOTA and 44.6 IDF1, while revealing fundamental differences between fish tracking and terrestrial object tracking scenarios. The dataset and codes are released at https://vranlee.github.io/SU-T/.
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