DETECTION FEATURES AS ATTENTION (DEFAT): A KEYPOINT-FREE APPROACH TO AMUR TIGER RE-IDENTIFICATION

Automatically identifying animals in camera-trap images has attracted increasing attention due to its valuable potential in wildlife conservation. A typical pipeline of existing methods includes separated animal detection and re-identification modules, and state-of-the-art re-identification methods either use annotated keypoints of animals to extract robust features, or employ extra branches to learn multiple features. In this paper, in contrast, we propose a keypoint-free approach to Amur tiger re-identification by exploiting the feature maps extracted by the detection module to help the re-identification module learn more effective features. We devise a detection-features-as-attention (DeFAt) module, which generates an additive mask for the input image based on the detection feature maps. We experimentally show that using the masked image the re-identification module lays more attention on the tiger region in the image, while the distraction by the messy background is removed to some extent. Our evaluation results prove that the proposed DeFAt module can effectively improve the Amur tiger re-identification accuracy when keypoint annotations are not available.