Summary of Fmrft: Fusion Mamba and Detr For Query Time Sequence Intersection Fish Tracking, by Mingyuan Yao et al.
FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking
by Mingyuan Yao, Yukang Huo, Qingbin Tian, Jiayin Zhao, Xiao Liu, Ruifeng Wang, Lin Xue, Haihua Wang
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel deep learning-based approach for real-time multi-target tracking of fish in industrial aquaculture. The authors address the challenges of underwater reflections, rapid swimming, and mutual occlusion by introducing the FMRFT model, which incorporates the Mamba In Mamba (MIM) architecture and a Query Time Sequence Intersection (QTSI) module. The proposed model effectively manages occluded objects, reduces redundant tracking frames, and enhances accuracy and stability. Trained on a complex multi-scenario sturgeon tracking dataset, the FMRFT model achieves an IDF1 score of 90.3% and a MOTA accuracy of 94.3%. This advance has significant implications for industrial aquaculture, enabling early detection of abnormal fish behavior caused by disease or hunger. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to use special computer learning methods to track groups of fish in large tanks. The problem is that the fish move quickly and sometimes hide behind each other, making it hard to keep track of them all. To solve this, the authors create a new kind of model that can remember what happened before and use that information to predict where the fish will be now. This helps the model to not get confused when multiple fish are in the same spot or moving quickly. The model is tested on real data and works well, which could help people who raise fish for food to detect if any of their fish are sick or hungry. |
Keywords
» Artificial intelligence » Deep learning » Tracking
