Video-based Locomotion Analysis for Fish Health Monitoring
This system provides a method for early disease detection and welfare monitoring in aquaculture by analyzing fish locomotion, benefiting fish farmers and researchers.
This paper presents a video-based system using multi-object tracking with a YOLOv11 detector to estimate locomotion activities of fish. The system reliably measures swimming direction and speed, which can be used for fish health monitoring.
Monitoring the health conditions of fish is essential, as it enables the early detection of disease, safeguards animal welfare, and contributes to sustainable aquaculture practices. Physiological and pathological conditions of cultivated fish can be inferred by analyzing locomotion activities. In this paper, we present a system that estimates the locomotion activities from videos using multi object tracking. The core of our approach is a YOLOv11 detector embedded in a tracking-by-detection framework. We investigate various configurations of the YOLOv11-architecture as well as extensions that incorporate multiple frames to improve detection accuracy. Our system is evaluated on a manually annotated dataset of Sulawesi ricefish recorded in a home-aquarium-like setup, demonstrating its ability to reliably measure swimming direction and speed for fish health monitoring. The dataset will be made publicly available upon publication.