The tracking of various fish species plays a profoundly significant role in
understanding the behavior of individual fish and their groups. Present
tracking methods suffer from issues of low accuracy or poor robustness. In
order to address these concerns, this paper proposes a novel tracking approach,
named FishMOT (Fish Multiple Object Tracking). This method combines object
detection techniques with the IoU matching algorithm, thereby achieving
efficient, precise, and robust fish detection and tracking. Diverging from
other approaches, this method eliminates the need for multiple feature
extractions and identity assignments for each individual, instead directly
utilizing the output results of the detector for tracking, thereby
significantly reducing computational time and storage space. Furthermore, this
method imposes minimal requirements on factors such as video quality and
variations in individual appearance. As long as the detector can accurately
locate and identify fish, effective tracking can be achieved. This approach
enhances robustness and generalizability. Moreover, the algorithm employed in
this method addresses the issue of missed detections without relying on complex
feature matching or graph optimization algorithms. This contributes to improved
accuracy and reliability. Experimental trials were conducted in the open-source
video dataset provided by idtracker.ai, and comparisons were made with
state-of-the-art detector-based multi-object tracking methods. Additionally,
comparisons were made with idtracker.ai and TRex, two tools that demonstrate
exceptional performance in the field of animal tracking. The experimental
results demonstrate that the proposed method outperforms other approaches in
various evaluation metrics, exhibiting faster speed and lower memory
requirements. The source codes and pre-trained models are available at:
this https URL

该研究提出了一种名为 FishMOT 的新型鱼类多目标跟踪方法，该方法将目标检测技术与 IoU 匹配算法相结合，实现了高效、精确和稳健的鱼类检测和跟踪，显著减少了计算时间和存储空间。实验结果表明，该方法在各种评估指标上优于其他方法，速度更快，内存需求更低。