A Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data
This work addresses the problem of tracking fish in ecological studies, offering an incremental improvement over single-view methods for domain-specific applications.
The paper tackled multi-object tracking of small fish in underwater environments by adapting single-view MOT models into a multi-view framework using stereo video inputs, achieving a relative accuracy of 47% for fish detection and producing 3D outputs for better movement analysis.
Multi-object tracking (MOT) in computer vision has made significant advancements, yet tracking small fish in underwater environments presents unique challenges due to complex 3D motions and data noise. Traditional single-view MOT models often fall short in these settings. This thesis addresses these challenges by adapting state-of-the-art single-view MOT models, FairMOT and YOLOv8, for underwater fish detecting and tracking in ecological studies. The core contribution of this research is the development of a multi-view framework that utilizes stereo video inputs to enhance tracking accuracy and fish behavior pattern recognition. By integrating and evaluating these models on underwater fish video datasets, the study aims to demonstrate significant improvements in precision and reliability compared to single-view approaches. The proposed framework detects fish entities with a relative accuracy of 47% and employs stereo-matching techniques to produce a novel 3D output, providing a more comprehensive understanding of fish movements and interactions