Abstract
Conference Title: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) Conference Start Date: 2016, Nov. 29 Conference End Date: 2016, Dec. 2 Conference Location: Agadir, Morocco 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 large view angle, pose, background clutter and occlusion and low resolution. Most of existing approaches rely on brute-force matching between pedestrian local descriptors and thus suffer from low computational efficiency. To address this issue, we present a new perspective for person re-identification based on a histogram encoding scheme [1] that assigns a global signature to each pedestrian image and thus simplifies the matching process. For that, an extended weighted version of the traditional Fisher vector encoding scheme is proposed. This is achieved by incorporating the Topological location of the encoded descriptors in the encoding process. Thus, two main contributions are proposed. (1) By designing a super Fisher vector representation, we aim to improve both the rate and the speedup of the person matching process. (2) By weighting the Fisher vector encoding scheme, we aim to remove noisy and busy background clutters surrounding a person throughout the Topological weight. Experimental results made on two challenging datasets, the VIPeR dataset, the CUHK03 dataset and the Market-1501 dataset, prove the effectiveness of the proposed method.