HOB-net: high-order block network via deep metric learning for person re-identification

Learning effective feature representations with deep convolutional neural network (CNN) and metric learning methods to distinguish pedestrians is the key to the success of recent advances in person re-identification (re-ID) tasks. However, the features provided by the common CNN network are not strong enough to distinguish between similar subjects because these common features describe only the scattered local patterns while neglecting the correlation and combinations between them. In fact, the high-order correlations of common features can be significant to the recognition of identities. In this paper, to tackle this problem, we design a flexible high-order block (HOB) module and a scheme of deep metric learning to produce the high-order representations of deep features for the re-identification of pedestrians. Extensive experiments prove the superiority of our proposed HOB module for person re-ID issue. On three large-scale datasets, including Market-1501, DukeMTMC-ReID, and CUHK03-NP, the HOB-net method achieves the competitive results with the state-of-the-arts, particularly in the mAP.