# A Multi-Fish Tracking and Behavior Modeling Framework for High-Density Cage Aquaculture

## TL;DR

This paper introduces SOD-SORT, a new framework for tracking and analyzing fish behavior in deep-sea cages by combining advanced motion models with appearance features.

## Contribution

The novel integration of a CTRV motion model with DeepOCSORT and optimized EKF parameters for multi-fish tracking in challenging cage environments.

## Key findings

- SOD-SORT achieves an IDF1 score of 0.829 and reduces identity switches by 13% compared to DeepOCSORT.
- A statistical quantization method enables unsupervised classification of normal and abnormal fish behaviors.
- Careful parameter optimization is critical to resolving motion-appearance conflicts in tracking performance.

## Abstract

Multi-fish tracking and behavior analysis in deep-sea cages face two critical challenges: first, the homogeneity of fish appearance and low image quality render appearance-based association unreliable; second, standard linear motion models fail to capture the complex, nonlinear swimming patterns (e.g., turning) of fish, leading to frequent identity switches and fragmented trajectories. To address these challenges, we propose SOD-SORT, which integrates a Constant Turn-Rate and Velocity (CTRV) motion model within an Extended Kalman Filter (EKF) framework into DeepOCSORT, a recent observation-centric tracker. Through systematic Bayesian optimization of the EKF process noise (Q), observation noise (R), and ReID weighting parameters, we achieve harmonious integration of advanced motion modeling with appearance features. Evaluations on the DeepBlueI validation set show that SOD-SORT attains IDF1 = 0.829 and reduces identity switches by 13% (93 vs. 107) compared to the DeepOCSORT baseline, while maintaining comparable MOTA (0.737). Controlled ablation studies reveal that naive integration of CTRV-EKF with default parameters degrades performance substantially (IDs: 172 vs. 107 baseline), but careful parameter optimization resolves this motion-appearance conflict. Furthermore, we introduce a statistical quantization method that converts variable-length trajectories into fixed-length feature vectors, enabling effective unsupervised classification of normal and abnormal swimming behaviors in both the Fish4Knowledge coral reef dataset and real-world Deep Blue I cage videos. The proposed approach demonstrates that principled integration of advanced motion models with appearance cues, combined with high-quality continuous trajectories, can support reliable behavior modeling for aquaculture monitoring applications.