[1U3-IS-2a] Machine learning

[1U3-IS-2a-05] Performance Evaluation of SORT Algorithms in Tracking of Fish in a School

〇Alin Khaliduzzaman¹, Takato Shibayama¹, Hitoshi Habe², Takayuki Niizato³, Hiroaki Kawashima¹ (1. Graduate School of Information Science, University of Hyogo, 2. Kindai University, 3. University of Tsukuba)

[[Online, Working-in-progress]]

Keywords:fish school, computer vision, multiple object tracking , real-time, collective behavior analysis

Real-time detection and tracking of fish in a school might have multifold functions to contribute to collective behavior analysis and precision fish farming practices like individual monitoring for growth, anomaly detection, population counting, feed management, and guided and directional control for various measurements. Although the method called SORT (simple online and real-time tracking) and its extensions (e.g., SORT, OC-SORT) are widely used for tracking humans (e.g., pedestrians), such algorithms might have a great challenge to track objects with a similar appearance (e.g., animals). Among them, tracking fish in a school is much more difficult because of their similarity in size, shape, and appearance. Therefore, this research aims to compare the performance of multiple object tracking (MOT) methods, specifically SORT and its latest extension, OC-SORT, to find suitable algorithms for further applied research on various physical and behavioral measurements of fish for individual, collective behavioral analysis, and precision fish farming practices in the future.