Multi-Level Common Space Learning for Person Re-Identification

Abstract

Matching people in different cameras, commonly referred to as person re-identification, is a challenging task. The challenges come from the drastic appearance variation across different camera views caused by changes in pose, lighting condition, occlusion, background, and so on. Instead of matching images in the original feature space, many existing methods learn distance metrics or feature transformations to improve the matching accuracy. For example, data from different camera views can be projected onto their common space to mitigate the feature gap caused by view discrepancy. However, a single-step mapping may not be sufficient to close this gap as the view difference can be significant between different cameras. To overcome this limitation, we propose a multi-level common space learning framework, which can gradually minimize the data discrepancy between different views in an iterative manner. At each intermediate level, synthetic data are generated using the automatically discovered grouping information, and these synthetic data can be viewed as a transitional state between the original camera views. The matching is performed by mapping the probe and gallery onto their common space in multiple steps. We evaluate the proposed method on two widely used person re-identification data sets. The results show that the proposed multi-level scheme significantly outperforms single-level mapping. In addition, competitive results are achieved as compared with over 20 state-of-the-art techniques.