# FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching

arXiv: https://arxiv.org/abs/2309.02975 · HTML: https://mlsanity.com/paper/2309.02975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019)
- [How To Train Your Deep Multi-Object Tracker](https://mlsanity.com/paper/1906.06618/) (2019)
- [Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space](https://mlsanity.com/paper/2211.14317/) (2022)
- [DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association](https://mlsanity.com/paper/2202.12100/) (2022)
- [DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction](https://mlsanity.com/paper/1908.03918/) (2019)
- [ToxTrac: a fast and robust software for tracking organisms](https://mlsanity.com/paper/1706.02577/) (2017)

## Built on by (most cited first, 1 of 1)

- [AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock](https://mlsanity.com/paper/2507.22101/) (2025)

## Abstract (arXiv)

Fish tracking plays a vital role in understanding fish behavior and ecology. However, existing tracking methods face challenges in accuracy and robustness dues to morphological change of fish, occlusion and complex environment. This paper proposes FishMOT(Multiple Object Tracking for Fish), a novel fish tracking approach combining object detection and IoU matching, including basic module, interaction module and refind module. Wherein, a basic module performs target association based on IoU of detection boxes between successive frames to deal with morphological change of fish; an interaction module combines IoU of detection boxes and IoU of fish entity to handle occlusions; a refind module use spatio-temporal information uses spatio-temporal information to overcome the tracking failure resulting from the missed detection by the detector under complex environment. FishMOT reduces the computational complexity and memory consumption since it does not require complex feature extraction or identity assignment per fish, and does not need Kalman filter to predict the detection boxes of successive frame. Experimental results demonstrate FishMOT outperforms state-of-the-art multi-object trackers and specialized fish tracking tools in terms of MOTA, accuracy, computation time, memory consumption, etc.. Furthermore, the method exhibits excellent robustness and generalizability for varying environments and fish numbers. The simplified workflow and strong performance make FishMOT as a highly effective fish tracking approach. The source codes and pre-trained models are available at: https://github.com/gakkistar/FishMOT