YakReID-103: A Benchmark for Yak Re-Identification

Abstract

Precision livestock management requires animal traceability and disease trajectory, for which discriminating between or re-identifying individual animals is of significant importance. Existing re-identification (re-ID) methods are mostly proposed for persons and vehicles, compared with which animals are extraordinarily more challenging to be re-identified because of subtle visual differences between individuals. In this paper, we focus on image-based re-ID of yaks (Bos grunniens), which are indispensable livestock in local animal husbandry economy in Qinghai-Tibet Plateau. We establish the first yak re-ID dataset (called YakReID-103) which contains 2, 247 images of 103 different yaks with bounding box, direction-based pose, and identity annotations. Moreover, according to the characteristics of yaks, we modifiy several person re-ID and animal re-ID methods as baselines for yak re-ID. Experimental results of the baselines on YakReID-103 demonstrate the challenges in yak re-ID. We expect that the proposed benchmark will promote the research of animal biometrics and extend the application scope of re-ID techniques.