Video-based Locomotion Analysis for Fish Health Monitoring

2026-03-05

AI Summary

This study addresses the challenge of health monitoring in farmed fish by proposing a non-invasive, video-based behavioral analysis method that enables early disease detection through the identification of abnormal locomotion. The approach employs the YOLOv11 object detector within a tracking-by-detection framework, enhanced by a multi-frame temporal information fusion strategy to accurately estimate the swimming direction and velocity of Sulawesi medaka (Oryzias woworae) in home aquarium environments. To the best of our knowledge, this work presents the first application of YOLOv11 to fish behavior analysis and introduces the first publicly available, manually annotated motion dataset for Sulawesi medaka. Experimental results demonstrate that the system reliably extracts key kinematic parameters on this custom dataset, offering an effective tool for intelligent aquatic health monitoring.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Time-Series/Data Streams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• fish health monitoring
• video-based locomotion analysis
• swimming behavior
• aquaculture

Innovation

• YOLOv11
• multi-object tracking
• video-based locomotion analysis
• fish health monitoring
• multi-frame detection