Video-based Locomotion Analysis for Fish Health Monitoring
- URL: http://arxiv.org/abs/2603.05407v1
- Date: Thu, 05 Mar 2026 17:32:46 GMT
- Title: Video-based Locomotion Analysis for Fish Health Monitoring
- Authors: Timon Palm, Clemens Seibold, Anna Hilsmann, Peter Eisert,
- Abstract summary: We present a system that estimates the locomotion activities from videos using multi object tracking.<n>Our system is evaluated on a manually annotated dataset of Sulawesi ricefish recorded in a home-aquarium-like setup.
- Score: 9.658086787950777
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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