Attributes co-occurrence pattern mining for video-based person re-identification
Person re-identification has received considerable attention in the image processing, computer vision and pattern recognition communities because of its huge potential for video-based surveillance applications and the challenges it presents due to illumination, pose and viewpoint changes among non-overlapping cameras. Being different from the widely used low-level descriptors, visual attributes (e.g., hair and shirt color) offer a human understandable way to recognize people. In this paper, a new way to take advantage of them is proposed. First, convolutional neural networks are adopted to detect the attributes. Second, the dependencies among attributes are obtained by mining association rules, and they are used to refine the attributes classification results. Third, metric learning technique is used to transfer the attribute learning task to person re-identification. Finally, the approach is integrated into an appearance-based method for video-based person re-identification. Experimental results on two benchmark datasets indicate that attributes can provide improvements both in accuracy and generalization capabilities.