Person Re-identification on Heterogeneous Camera Network

Person re-identification (re-id) aims at matching person images across multiple surveillance cameras. Currently, most re-id systems highly rely on color cues, which are only effective in good illumination conditions, but fail in low lighting conditions. However, for security issues, it is very important to conduct surveillance in low lighting conditions. To remedy this problem, we propose using depth cameras to perform surveillance in dark places, while using traditional RGB cameras in bright places. Such a heterogeneous camera network brings a challenge to match images across depth and RGB cameras. In this paper, we mine the correlation between two heterogeneous cues (depth and RGB) on both feature-level and transformation-level. As such, depth-based features and RGB-based features are transformed to the same space, which alleviates the problem of cross-modality matching between depth and RGB cameras. Experimental results on two benchmark heterogeneous person re-id datasets show the effectiveness of our method.