A Re-ID and Tracking-by-detection Framework for Multiple Wildlife Tracking with Artiodactyla Characteristics in Ecological Surveillance

Hongpeng Wang, Jinchao Zhu, Wan Dai, Jingtai Liu

Abstract: Long-term non-interventional observation of wild animals in the natural environment is very necessary for ecological protection. With the development of artificial intelligence, it is possible to effectively utilize the features of wild artiodactyls and realize multi-target tracking and reidentification. In this paper, a re-identification and tracking-by-detection framework is proposed for real-time tele-observation of Artiodactyla. According to the characteristics of artiodactyla, our algorithm designed a three-direction feature extraction and feature matching method to achieve re-identification. The kalman filter is used in cooperation with the detector to confirm the presence of the target. Our framework integrates detector, kalman tracker and KCF tracker, which alleviates the problem of discontinuous detection results caused by the low detection rate of artiodacods in the field environment. In the KCF tracking process, the tracking bounding boxes are corrected with high confidence detection results. The experiments demonstrate the feasibility and effectiveness of the framework.