When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking

The official implementation of the paper：

When Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking
Weiran Li, Yeqiang Liu, Qiannan Guo, Yijie Wei, Hwa Liang Leo, Zhenbo Li* [Project] [Paper] [Code]

Contact: [email protected] or [email protected]. Any questions or discussion are welcome!

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📌Updates

[2025.07] Paper released to arXiv.
[2025.07] Fixed bugs.
[2025.04] We have released the MFT25 dataset and codes of SU-T!

💡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. 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. MFT25 establishes a robust foundation for advancing research in underwater tracking systems with important applications in marine biology, aquaculture monitoring, and ecological conservation.

🏆Contributions

We introduce MFT25, the first comprehensive multiple fish tracking dataset featuring 15 diverse video sequences with 408,578 meticulously annotated bounding boxes across 48,066 frames, capturing various underwater environments, fish species, and challenging conditions including occlusions, rapid direction changes, and visually similar appearances.
We propose 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 and erratic movement behaviors of aquatic species.
We conduct extensive comparative experiments demonstrating that our tracker achieves state-of-the-art performance on MFT25, with 34.1 HOTA and 44.6 IDF1. Through quantitative analysis, we highlight the fundamental differences between fish tracking and land-based object tracking scenarios.

🛠️Installation Guide

Prerequisites

CUDA >= 10.2
Python >= 3.7
PyTorch >= 1.7.0
Ubuntu 18.04 or later (Windows is also supported but may require additional setup)

Step-by-Step Installation

Clone the Repository

git clone https://github.com/vranlee/SU-T.git
cd SU-T

Create and Activate Conda Environment

# Create environment from yaml file
conda env create -f conda_env.yaml

# Activate the environment
conda activate su_t

Download Required Resources
- Download pretrained models from BaiduYun (Password: 9uqc)
- Download MFT25 dataset from BaiduYun (Password: wrbg)

Organize the Directory Structure

SU-T/
├── pretrained/
│   └── Checkpoint.pth.tar
├── MFT25/
│   ├── train/
│   └── test/
└── ...

🍭Usage Guide

Training

Basic Training Command

python tools/train.py \
    -f exps/SU-T.py \        # Base model configuration
    -d 8 \                   # Number of GPUs
    -b 48 \                  # Batch size
    --fp16 \                 # Enable mixed precision training
    -o \                     # Enable occupy GPU memory
    -c pretrained/Checkpoint.pth.tar  # Path to pretrained weights

Training with ReID Module

python tools/train.py \
    -f exps/SU-T-ReID.py \   # ReID model configuration
    -d 8 \
    -b 48 \
    --fp16 \
    -o \
    -c pretrained/Checkpoint.pth.tar

Testing

Basic Testing Command

python tools/su_tracker.py \
    -f exps/SU-T.py \        # Model configuration
    -b 1 \                   # Batch size
    -d 1 \                   # Number of GPUs
    --fp16 \                 # Enable mixed precision
    --fuse \                 # Enable model fusion
    --expn your_exp_name     # Experiment name

Testing with ReID Module

python tools/su_tracker.py \
    -f exps/SU-T-ReID.py \   # ReID model configuration
    -b 1 \
    -d 1 \
    --fp16 \
    --fuse \
    --expn your_exp_name

Additional Configuration Options

Model Configuration: Edit exps/SU-T.py or exps/SU-T-ReID.py to modify:
- Learning rate
- Training epochs
- Data augmentation parameters
- Model architecture settings

Training Parameters:

# Additional training options
--cache        # Cache images in RAM
--resume       # Resume from a specific checkpoint
--trt          # Export TensorRT model

Testing Parameters:

# Additional testing options
--tsize        # Test image size
--conf         # Confidence threshold
--nms          # NMS threshold
--track_thresh # Tracking threshold

📜Tracking Performance

Comparisons on MFT25 dataset

Method	Class	Year	HOTA↑	IDF1↑	MOTA↑	AssA↑	DetA↑	IDs↓	IDFP↓	IDFN↓	Frag↓
FairMOT	JDE	2021	22.226	26.867	47.509	13.910	35.606	939	58198	113393	3768
CMFTNet	JDE	2022	22.432	27.659	46.365	14.278	35.452	1301	64754	111263	2769
TransTrack	TF	2021	30.426	35.215	68.983	18.525	50.458	1116	96045	93418	2588
TransCenter	TF	2023	27.896	30.278	68.693	30.255	30.301	807	101223	101002	1992
TrackFormer	TF	2022	30.361	35.285	74.609	17.661	52.649	718	89391	94720	1729
TFMFT	TF	2024	25.440	33.950	49.725	17.112	38.059	719	63125	102378	3251
SORT	SDE	2016	29.063	34.119	69.038	16.952	50.195	778	88928	96815	1726
ByteTrack	SDE	2022	31.758	40.355	69.586	20.392	49.712	489	80765	87866	1555
BoT-SORT	SDE	2022	26.848	36.847	49.108	19.446	37.241	500	57581	99181	2704
OC-SORT	SDE	2023	25.017	34.620	46.706	17.783	35.369	550	52934	103495	3651
Deep-OC-SORT	SDE	2023	24.848	34.176	46.721	17.537	35.373	550	53478	104024	3659
HybridSORT	SDE	2024	32.258	38.421	68.905	20.936	49.992	613	85924	90022	1931
HybridSORT†	SDE	2024	32.705	41.727	69.167	21.701	49.697	562	79189	85830	1963
SU-T (Ours)	SDE	2025	33.351	41.717	68.450	22.425	49.943	607	83111	84814	2006
SU-T† (Ours)	SDE	2025	34.067	44.643	68.958	23.594	49.531	544	76440	81304	2011

*Note: † indicates the integration of ReID module, Bold indicates the best performance, italics indicate the second-best performance

⁉️Troubleshooting

Common Issues

CUDA Out of Memory
- Reduce batch size
- Use smaller input resolution
- Enable mixed precision training
Installation Failures
- Ensure CUDA toolkit matches PyTorch version
- Try creating environment with pip if conda fails
- Check system CUDA compatibility
Training Issues
- Verify dataset path and structure
- Check GPU memory usage
- Monitor learning rate and loss curves

💕Acknowledgement

A large part of the code is borrowed from ByteTrack, OC_SORT, and HybridSORT. Thanks for their wonderful works!

📖Citation

The citation format will be given after the manuscript is accepted. Using arXiv's citation if needed now.

📑License

This project is released under the MIT License.

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