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Fish tracking plays a vital role in understanding fish behavior and ecology.However, existing tracking methods face challenges in accuracy and robustnessdues to morphological change of fish, occlusion and complex environment. Thispaper proposes FishMOT(Multiple Object Tracking for Fish), a novel fishtracking approach combining object detection and IoU matching, including basicmodule, interaction module and refind module. Wherein, a basic module performstarget association based on IoU of detection boxes between successive frames todeal with morphological change of fish; an interaction module combines IoU ofdetection boxes and IoU of fish entity to handle occlusions; a refind moduleuse spatio-temporal information uses spatio-temporal information to overcomethe tracking failure resulting from the missed detection by the detector undercomplex environment. FishMOT reduces the computational complexity and memoryconsumption since it does not require complex feature extraction or identityassignment per fish, and does not need Kalman filter to predict the detectionboxes of successive frame. Experimental results demonstrate FishMOT outperformsstate-of-the-art multi-object trackers and specialized fish tracking tools interms of MOTA, accuracy, computation time, memory consumption, etc..Furthermore, the method exhibits excellent robustness and generalizability forvarying environments and fish numbers. The simplified workflow and strongperformance make FishMOT as a highly effective fish tracking approach. Thesource codes and pre-trained models are available at:https://github.com/gakkistar/FishMOT

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