ArticleScientific reports2025
Synergistic enhancement of detection-tracking framework for zebrafish shoaling behavior analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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