Cross-view kernel collaborative representation classification for person re-identification

Currently, person re-identification (re-ID) has been applied in many public security applications. Yet owing to the big visual appearance changes of the same identity under different views, re-ID still faces many challenges. To reduce the intra-person discrepancy, extracting more power feature representations from pedestrian images is a reasonable solution. We propose a cross-view kernel collaborative representation based classification (CV-KCRC) method for person re-ID in our work. Our method aims to find more robust and discriminative feature representations that embody cross-view information to enhance the identification capability of features. We map the image features into a high dimensional feature space first and then use view-specific projection matrices to project the high dimensional features into a common low dimensional subspace. We expect that in the shared subspace the codings of same person from different views have the highest similarity and better performance can be achieved. Experiments on seven commonly used datasets reveal that our algorithm outperforms many state-of-the-art algorithms.

Automated summary

Person re-identification (re-ID) faces challenges due to big visual changes of the same individual under different views. Extracting powerful feature representations from pedestrian images is a reasonable solution. The proposed CV-KCRC method aims to find more robust and discriminative feature representations by projecting image features into a common low dimensional subspace, outperforming many state-of-the-art algorithms in experiments on seven commonly used datasets.