Multi-Panda Tracking

Abstract

Multi-Panda Tracking (MPT) is a video-based tracking task for panda individuals, which is conducive to the observation and measurement of distribution and status of pandas. Different from tracking general objects such as pedestrians and vehicles, MPT is extremely challenging due to the indistinguishable appearances and diversified postures of pandas. In this case, existing tracking methods cannot appropriately tackle with the excessive occlusion between different panda individuals, hence suffering from identity switch, missing and inaccurate detections. To address these problems, we propose a simple yet effective MPT framework in the tracking-by-detection paradigm, which is benefited both from a short-term prediction filtering module and a discriminative feature learning network. In particular, the short-term prediction filtering module introduces similarity learning to enhance the temporal consistency among detections, which is capable of supplementing the missing detections and discarding false positive detections. Besides, the discriminative feature learning network leverages a two-branch network to learn both local and global discriminative features, so as to distinguish different panda individuals with a very similar appearance with a subtle difference. To evaluate the proposed method, we annotate a large-scale MPT dataset, named PANDA2021, which is particularly challenging due to the similar appearance and dramatic occlusion between panda individuals. Experiments on PANDA2021 demonstrate that the proposed MPT method significantly outperforms the competing methods. Moreover, experimental results on pedestrian tracking dataset MOT16 further demonstrate that the proposed MPT method achieves comparative performance with competing methods.