Deep Spatial Pyramid Features Collaborative Reconstruction for Partial Person ReID

Abstract

Partial person re-identification (ReID) is a hot research problem in computer vision. Accurate partial ReID is very challenging due to the common occlusion problem. To address this problem, in this paper, we propose a novel D eep spatial pyramid feature C ollaborative R econstruction approach (DCR ) for partial person ReID, which can effectively and efficiently tackle the occlusion in arbitrary sizes. Specifically, a fully convolutional network (FCN) is first leveraged to extract feature maps of an arbitrary-size image, and then the spatial pyramid pooling (SPP) is adopted to obtain spatial pyramid features. Thereafter, our DCR method is designed to efficiently solve the matching problem between the partial person and the holistic person in the partial person ReID task where the occlusion problem often occurs. Experiments on two partial person ReID datasets demonstrate the efficiency and efficacy of the proposed method by comparing to several state-of-the-art partial person ReID approaches. Our method outperforms all the competitors with a large margin and can achieve an improvement of 9.07% and 5.95% over the DSR method on the Partial REID and Partial-iLIDS Person ReID datasets in terms of the Rank-1 accuracy, respectively.