Learning Rich Feature Representation and State Change Monitoring for Accurate Animal Target Tracking

Animal tracking is crucial for understanding migration, habitat selection, and behavior patterns. However, challenges in video data acquisition and the unpredictability of animal movements have hindered progress in this field. To address these challenges, we present a novel animal tracking method based on correlation filters. Our approach integrates hand-crafted features, deep features, and temporal context information to learn a rich feature representation of the target animal, enabling effective monitoring and updating of its state. Specifically, we extract hand-crafted histogram of oriented gradient features and deep features from different layers of the animal, creating tailored fusion features that encapsulate both appearance and motion characteristics. By analyzing the response map, we select optimal fusion features based on the oscillation degree. When the target animal's state changes significantly, we adaptively update the target model using temporal context information and robust feature data from the current frame. This updated model is then used for re-tracking, leading to improved results compared to recent mainstream algorithms, as demonstrated in extensive experiments conducted on our self-constructed animal datasets. By addressing specific challenges in animal tracking, our method offers a promising approach for more effective and accurate animal behavior research.