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Video-based Locomotion Analysis for Fish Health Monitoring

Timon Palm, Clemens Seibold, Anna Hilsmann, Peter Eisert

TL;DR

This paper presents a system that estimates the locomotion activities from videos using multi object tracking of YOLOv11 detector embedded in a tracking-by-detection framework and investigates various configurations of the YOLOv11-architecture as well as extensions that incorporate multiple frames to improve detection accuracy.

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.

Video-based Locomotion Analysis for Fish Health Monitoring

TL;DR

This paper presents a system that estimates the locomotion activities from videos using multi object tracking of YOLOv11 detector embedded in a tracking-by-detection framework and investigates various configurations of the YOLOv11-architecture as well as extensions that incorporate multiple frames to improve detection accuracy.

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.
Paper Structure (13 sections, 5 figures, 4 tables)

This paper contains 13 sections, 5 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHOD
  4. DATASET
  5. EXPERIMENTS
  6. Detection
  7. Metrics
  8. Results
  9. Tracking
  10. Metrics
  11. Results
  12. Swimming Direction
  13. CONCLUSIONS

Figures (5)

  • Figure 1: The Sulawesi ricefish: Its lucid appearance, small size and the large number of instances in a shoal makes accurate tracking difficult.
  • Figure 2: A schematic view of extracting quantitative directional information of fish observations from Tracking-By-Detection methods.
  • Figure 3: Example frames with segmentation annotation from our Sulawesi ricefish dataset.
  • Figure 4: Histograms depicting the angular swimming directions extracted from tracking with model size nano (left), medium (middle) and large (right) for video A (top row) and video B (bottom row).
  • Figure 5: Histograms showing the aggregated magnitude of the swimming directions from tracking with model size nano (left), medium (middle) and large (right) for video A (top row) and video B (bottom row).