Anchor-supported multi-modality hashing embedding for person re-identification

Abstract

Person re-identification is a challenging problem in multi-camera surveillance systems. Most existing methods focus on metric learning which aims to match images from different cameras in a common metric space. Boosted hashing projection provides a new way of identifying instances based on pairwise similarity. However, both of these approaches ignore the underlying fact that images captured by two cameras should be seen as in different modalities. To address this drawback, we formulate person re-identification as an Anchor-supported Multi-Modality Hashing Embedding (AMMHE) problem, in which different projections are used to map data from different cameras into a common Hamming space. The data are projected to binary bits by using boosted hash projections, making the weighted Hamming distance of intra-class data pairs minimized and simultaneously those of inter-class data pairs maximized. We also introduce an anchor-supported dimension reduction method to avoid the computational burden of high feature dimensionality. Our approach obtains competitive performance compared with state-of-the-art methods on publicly available benchmarks.