Keypoint-Based Feature Matching for Partial Person Re-Identification

As a derivative of person re-identification (re-ID), partial re-ID aims to retrieve a partial pedestrian across holistic person images captured by non-overlapping cameras. This task is more challenging and closer to real-world applications. Since we cannot locate the part of the partial image, the misaligned region compromises the performance greatly when directly compare a partial pedestrian with a holistic one. To alleviate this issue, we propose a Keypoint-Based Feature Matching (KBFM) network, which constructs a simple and effective framework for partial re-ID. Specifically, our architecture explicitly leverages the keypoints generated by pose estimation. Based on the visible keypoints, coordinates of the corresponding visible region can be computed. And the keypoint-based feature embeddings can be generated by bilinear sampling. When matching two images, we extract their features on the basis of shared visible keypoints, avoiding the misalignment and disturbance. Moreover, considering the triplet loss cannot be flexibly built in the partial re-ID pipeline, we improve the original sampling method and achieve significant performance. Extensive experimental results on two widely used benchmarks demonstrate significant performance improvements of our method over most state-of-the-art methods.