Tensor-based sparse canonical correlation analysis via low rank matrix approximation for RGB-D long-term person re-identification

Person re-identification can be a part of almost any multi-camera surveillance systems. Most previous works propose strategies for short-term person re-identification which are usually driven from appearance features of RGB images. However, when people appear in excessive lighting or change clothes (i.e. long-term case), short-term person re-identification approaches have a tendency to fail. In this paper, we propose a novel approach for long-term person re-identification by employing depth videos of RGB-D sensors. We also develop a sparse canonical correlation analysis using a local third-order tensor model to accomplish multi-level person re-identification. The tensor representations of images make the space for performing the multi-level person re-identification simpler compared to existing methods. Finally, we evaluate our experiments on RGB-D long-term datasets consisting of BIWI RGBD-ID dataset and IAS-Lab RGBD-ID dataset. The results demonstrate the efficiency of the proposed method compared to recent methods.