Exploiting low-rank structures from cross-camera images for robust person re-identification

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Matching individuals across non-overlapping camera views is known as the problem of person re-identification. In addition to significant visual appearance variations due to lighting, view angle, etc. changes, one might encounter corrupted data due to background clutter and occlusion, or even missing data at some camera views in practical scenarios. To address the above challenges, we present a novel approach to robust person re-identification, particularly aiming at handling missing and corrupted image data across camera views. Based on the technique of low-rank matrix decomposition, our proposed algorithm observes the low-rank structure of cross-view data, which is able to disregard extreme/sparse errors while the missing instances can be recovered automatically. Our experiments will confirm the effectiveness and robustness of our method, which is shown to outperform several baseline and state-of-the-art person re-identification approaches.