FishTracker: An Efficient Multi-Object Tracking Algorithm for Fish Monitoring in a RAS Environment

Understanding fish movement trajectories in aquaculture is essential for practical applications, such as disease warning, feeding optimization, and breeding management. These trajectories reveal key information about the fish’s behavior, health, and environmental adaptability. However, when multi-object tracking (MOT) algorithms are applied to the high-density aquaculture environment, occlusion and overlapping among fish may result in missed detections, false detections, and identity switching problems, which limit the tracking accuracy. To address these issues, this paper proposes FishTracker, a MOT algorithm, by utilizing a Tracking-by-Detection framework. First, the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention (MSDA) module to improve object localization and classification confidence. Second, an Adaptive Kalman Filter (AKF) is employed in the tracking phase to dynamically adjust motion prediction parameters, thereby overcoming target adhesion and nonlinear motion in complex scenarios. Experimental results show that FishTracker achieves a multi-object tracking accuracy (MOTA) of 93.22% and 87.24% in bright and dark illumination conditions, respectively. Further validation in a real aquaculture scenario reveal that FishTracker achieves a MOTA of 76.70%, which is 5.34% higher than the baseline model. The higher order tracking accuracy (HOTA) reaches 50.5%, which is 3.4% higher than the benchmark. In conclusion, FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.