DCR: A Unified Framework for Holistic/Partial Person ReID

Abstract

Person reidentification (ReID) is a very popular research topic in machine learning and computer vision. According to the occlusions, it can be divided into holistic person ReID and partial person ReID tasks. Occlusions commonly exist in the partial person ReID task but pose or observation perspective changes often occur in the holistic person ReID task; thus, many different algorithms or different network architectures have been designed for each task. However, this approach increases the cost in practice and hinders the development of ReID techniques. To solve this problem, in this work, a unified framework is proposed for holistic/partial person ReID, which can effectively and efficiently address changes in pose or observation perspective and the occlusions in both tasks. In detail, we first employ a fully convolutional network (FCN) to generate feature maps for an arbitrarily sized image and then use spatial pyramid pooling (SPP) to obtain its spatial pyramid feature. Thereafter, to efficiently solve the matching problem between the query image and gallery images, we build a deep spatial pyramid feature collaborative reconstruction model (DCR). In DCR, the reconstruction errors mainly come from similar blocks (uncovered parts), and the influence of the reconstruction errors of dissimilar blocks (covered parts or changed parts) is minimized. In addition, we also use the deep mutual learning approach to jointly learn the features in the training process and promote model training. Experimental results on two partial person ReID datasets and three holistic person ReID datasets demonstrate that the DCR outperforms the state-of-the-art approaches on both tasks and all datasets. Specifically, it outperforms all competitors with a large margin and achieves an improvement of 9.07% and 5.95% over the DSR method (published in CVPR18) on the Partial ReID and Partial-iLIDS datasets with Rank-1, respectively. Similarly, it also achieves an improvement of 5.08% over the VPM method (published in CVPR19) on the DukeMTMC-ReID dataset with Rank-1. Additionally, the running time of our method for each query is more than 7 faster than that of the DSR or DuATM methods.