Single-shot person re-identification based on improved Random-Walk pedestrian segmentation

Single-shot person re-identification is to match pedestrian images captured from different cameras at different time under the condition of large illumination variations, different viewpoints, and inadequate information of single-shot case. To deal with these challenges, we propose a four-step single-shot person re-identification algorithm that consists of pedestrian segmentation, human region partitioning, feature extraction and human feature matching. Based on an improved Random Walks algorithm, human foreground is segmented by combining the shape prior information and the color seed constraint into the Random Walk formulation. Then color features of HSV histogram and 1-D RGB signal along with texture features from human body parts are used for the person re-identification. The correct match is then determined by the similarity scores of all features with appropriate weight selection. The experimental results demonstrate the superior performance by using the proposed algorithm compared to the previous representative methods.