Person Re-Identification Based on Heterogeneous Part-Based Deep Network in Camera Networks

Abstract

In this paper, we propose a new deep learning model named heterogeneous part-based deep network for person re-identification in camera networks, which simultaneously learns the alignment and discrimination for parts of pedestrian images. Concretely, several parts are obtained through the uniform partition on the convolutional layer for each pedestrian image. Then, we present part-aligned distances to perform alignment by searching the shortest local distances between image parts in a certain range. Meanwhile, we utilize the batch hard triplet loss and cross-entropy loss to learn more discriminative part-based features in different aspects. Experiments are conducted on three challenging datasets, Market-1501, CUHK03, and DukeMTMC-reID, and we achieve 94.0%, 64.3%, and 83.6% rank-1 accuracy and 81.2%, 58.2%, and 68.0% mAP, outperforming the state-of-the-art methods by a large margin.