Improving consistency of marine mammals tracking in challenging drone recordings through visual particle filter integration

Tracking marine mammals is essential for gaining insights into their health, behaviour, and population dynamics. With the growing use of drones in ecological research, the demand for efficient, automated video analysis has increased. This paper introduces a novel tracking algorithm designed explicitly for online marine mammals tracking in UAV-recorded videos, addressing unique challenges such as complex seabed textures, water reflections, and irregular animal movements. By integrating particle filters with the widely used Simple Online and Real-time Tracking (SORT) algorithm, we tackle issues of missed detections in marine contexts, enhancing tracking robustness. We also present a new publicly available dataset for drone-based porpoise tracking and evaluate our SORT-PF against state-of-the-art tracking methods. The results demonstrate significant improvements, including a 9.3% increase in the HOTA metric, simultaneously keeping the lowest ID switches compared to baseline methods, highlighting the potential of the proposed SORT-PF for automated wildlife monitoring and other challenging tracking applications.