Synergistic enhancement of detection-tracking framework for zebrafish shoaling behavior analysis

Abstract

Computer vision and artificial intelligence (AI) have become increasingly important in behavioral analysis across biological research. In contrast to well-established methods for individual behavior analysis, computational frameworks for quantitatively assessing zebrafish shoaling behavior remain limited. To address this gap, we propose a cascaded detection–tracking framework that integrates multi-scale object detection with adaptive motion tracking for zebrafish shoaling behavior analysis. A multidimensional feature set was developed to extract both kinematic and spatial distribution metrics from tracked trajectories. Behavioral analysis revealed a biphasic effect of ethanol: low concentrations increased global motion intensity (hyperactivity), whereas higher concentrations reduced locomotor activity and disrupted shoal cohesion.