Extended Salient Fisher Vector encoding for Person Re-identification

Recognizing the same person across multiple potentially non-overlapping cameras, known as Person Re-identification, is a fundamental challenging task in Computer Vision. This is due to the important challenges that it proposes, like view point and pose change, background clutter and occlusion, low resolution and illumination variations. Most of the existing approaches rely on brute-force matching between local descriptors and thus suffer from low computational efficiency. Therefore, we present a new perspective for person re-identification based on a histogram encoding scheme. For that, an extended weighted version of the traditional Fisher vector encoding scheme is proposed. This is achieved by incorporating the Salience of the encoded descriptors and their topological location in the encoding process. Experimental results made on three challenging datasets (VIPeR, CUHK03 and Market-1501 dataset), prove the effectiveness of the proposed method.