Abstract
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. We present Multiple Fish Tracking Dataset 2025 (MFT25), the first 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 fish swimming patterns and a novel Fish-Intersection-over-Union (FishIoU) matching that accounts for the unique morphological characteristics of aquatic species.
MFT25 Dataset
- First comprehensive benchmark for underwater MOT
- Includes both actual farm & laboratory collections
- Diverse range of fish species and turbidity conditions
- 48,066 frames meticulously annotated
SU-T Framework
- Unscented Kalman Filter (UKF) for non-linear motion
- Fish-IoU optimized for aquatic body shapes
- State-of-the-art performance on MFT25
- Robust against occlusion and rapid swimming
Performance Comparison
Downloads
Pretrained Models
Includes SU-T base model, SU-T with ReID module, and checkpoints.
BaiduYun (Pwd: 9uqc)