Partial Distance Correlation-Based Motion Pattern Detection in Pangasius Fish

Abstract

In computer vision, behavioral recognition of aquatic organisms plays an important role, particularly for Pangasius catfish, a fish species commonly cultured in Vietnam. This study presents a method for detecting Pangasius motion patterns comprising 38 catfish videos with a total of 236,133 extracted frames, from which 4,593 motion windows are extracted and classified into six behavioral categories (Cruising, Burst–Coast, Escape, Schooling, Milling, and Swarming) based on Partial Distance Correlation (PDC) integrated with video processing techniques and feature extraction methods. Experimental results show that Distance Correlation (dCor) on raw data yields high correlation values (0.826–0.989) but with substantial scatter. PDC with heading angle control maintains elevated values (0.804–0.979) with tighter residual clustering. When denoising is combined with heading angle control, pdCor achieves optimal efficacy (0.852–0.973). Compared with dCor, pdCor provides consistent improvements, especially for complex behaviors (Escape: 5.1%; Swarm: 3.2%). The combined strategy detects 40% of patterns better and 60% similarly, indicating pdCor does not reduce performance for simple behaviors but substantially improves detection for nonlinear, high noise patterns.