SMT: Self-supervised Approach for Multiple Animal Detection and Tracking

Abstract

In the domain of animal farming and wildlife management, monitoring animal behavior and movement is crucial. This paper proposes an efficient online multi-object tracking framework named SMT (Self-supervised Multi-animal detection and Tracking) for a dynamic and complex environment. The framework is based on the tracking-by-detection approach and builds on the idea of employing self-supervised object detection and a bag of Bayesian trackers. We collected and annotated a custom dataset from an animal farm for training and validating the detection and tracking algorithms. Additionally, we utilized the public dataset Dancetrack to benchmark and compare the results against reference methods. The comparison with reference methods reveals substantial enhancements on standard tracking metrics, such as IDF1 and MOTA. The optimized combination of the self-supervised object detector and proposed tracker demonstrates robust performance by consistently preserving object identities and reducing identification errors throughout sequences. To reproduce the results, we made the code publically available at https://github.com/moosa1296/effdet_ocsort.