Sparsity-Based Occlusion Handling Method for Person Re-identification

Person re-identification has recently attracted a lot of research interests, it refers to recognizing people across non-overlapping surveillance cameras. However, person re-identification is essentially a very challenging task due to variations in illumination, viewpoints and occlusions. Existing methods address these difficulties through designing robust feature representation or learning proper distance metric. Although these methods have achieved satisfactory performance in the case of illumination and viewpoint changes, seldom of they can genuinely handle the occlusion problem that frequently happens in the real scene. This paper proposes a sparsity-based patch matching method to handle the occlusion problem in the person re-identification. Its core idea is using a sparse representation model to determine the occlusion state of each image patch, which is further utilized to adjust the weight of patch pairs in the feature matching process. Extensive comparative experiments conducted on two widely used datasets have shown the effectiveness of the proposed method.