Easily accessible technologies, such as drones equipped with diverse onboard sensors, have greatly expanded opportunities to study animal behavior in natural environments. However, analyzing large volumes of unlabeled video data, often spanning hours, remains a significant challenge for machine learning, particularly in computer vision. Existing approaches typically process only a small number of frames, and accurate georeferencing of tracked positions is still largely unresolved, particularly in dynamic environments where static landmarks cannot be established. In this work, we focus on long-term tracking of animal behavior in real-world geographic coordinates. To address this challenge, we utilize classical probabilistic methods for state estimation, such as particle filtering. Particle filters offer a useful algorithmic structure for recursively adding new incoming information and thus ensuring time consistency. By incorporating recent developments in semantic object segmentation, we enable continuous tracking of rapidly evolving object formations, even in scenarios with limited data availability. We propose a novel approach for tracking schools of fish in the open ocean from drone videos. Our framework not only performs classical object tracking in image coordinates, instead it additionally tracks the position and spatial expansion of the fish school in geographic coordinates by fusing video data and the drone’s on board sensor information (GPS and IMU). No landmarks with known geographic coordinates are required, making the proposed method adaptable to unstructured, dynamic environments like the open ocean, where static landmarks are unavailable. With this, the presented framework enables researchers to study the collective behavior of fish schools within their social and environmental context.
Bideau et al. (Fri,) studied this question.