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

TL;DR

FishMOT is a new fish tracking method that combines IoU matching with modules for handling morphological changes, occlusions, and missed detections, offering high accuracy and efficiency in complex environments.

Contribution

It introduces a simple, effective fish tracking approach that reduces computational complexity without relying on complex feature extraction or Kalman filters.

Findings

Outperforms state-of-the-art trackers in accuracy and efficiency

Demonstrates robustness across various environments and fish numbers

Requires less computation and memory than existing methods

Abstract

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