Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification

Person re-identification (Re-ID) is an important real-world surveillance problem that entails associating a person's identity over a network of cameras. Video-based Re-ID approaches have gained significant attention recently since a video, and not just an image, is often available. In this work, we propose a novel Co-segmentation inspired video Re-ID deep architecture and formulate a Co-segmentation based Attention Module (COSAM) that activates a common set of salient features across multiple frames of a video via mutual consensus in an unsupervised manner. As opposed to most of the prior work, our approach is able to attend to person accessories along with the person. Our plug-and-play and interpretable COSAM module applied on two deep architectures (ResNet50, SE-ResNet50) outperform the state-of-the-art methods on three benchmark datasets.