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。

FishMOT: 基于 IoU 匹配的简单有效鱼类跟踪方法

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

We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for
detecting, tracking, and counting fish in sonar videos. We identify sonar
videos as a rich source of data for advancing low signal-to-noise computer
vision applications and tackling domain generalization in multiple-object
tracking (MOT) and counting. In comparison to existing MOT and counting
datasets, which are largely restricted to videos of people and vehicles in
cities, CFC is sourced from a natural-world domain where targets are not easily
resolvable and appearance features cannot be easily leveraged for target
re-identification. With over half a million annotations in over 1,500 videos
sourced from seven different sonar cameras, CFC allows researchers to train MOT
and counting algorithms and evaluate generalization performance at unseen test
locations. We perform extensive baseline experiments and identify key
challenges and opportunities for advancing the state of the art in
generalization in MOT and counting.

本文介绍了 Caltech 鱼类计数数据集（CFC），它是一种大规模的数据集，用于检测、跟踪和计数声纳视频中的鱼类。该数据集为低信噪比计算机视觉应用程序提供了丰富的数据来源，并且比现有的大多数计数数据集来源更自然。我们使用基线实验来评估 MOT 和计数中的泛化表现，并确定了进一步发展状态的关键挑战和机遇。