Accordion representation based multi-scale covariance descriptor for multi-shot person re-identification

Abstract

Multi-shot person re-identification is a major challenge because of the large variations in a human’s appearance caused by different types of noise such as occlusion, viewpoint and illumination variations. In this paper, we presented the accordion representation based multi-scale covariance descriptor, called AR-MSCOV descriptor, which considers in the first step an image sequence containing a walking human to convert it in one image with the accordion representation. To better exploit the spatial and temporal correlation of the video sequence and to deal with the different types of noise, it applies quadtree decomposition and extracts multi-scale appearance features such as color, gradient and Gabor in a simple pass. This AR-MSCOV descriptor merges the static regions and captures the moving regions of interest. Therefore, it implicitly encodes the described human gait as a behavioral biometric with the appearance features through the accordion representation to reliably identify any person in motion. We evaluated the AR-MSCOV descriptor on the PRID 2011 multi-shot dataset and demonstrated a good performance in comparison with the current state-of-the-art.