Topological Weighted Fisher Vectors For Person Re-Identification

Person re-identification is a fundamental challenging task in Computer Vision that consists on recognizing the same person across multiple potentially non-overlapping cameras. This importance is due to the important challenges that it proposes like pose, background clutter and occlusion, illumination changes and low resolution. Also, most of the existing approaches rely on brute-force matching between pedestrian local descriptors and consequently, suffer from low computational efficiency. So, to address this issues, we present a new perspective for person re-identification based on a histogram encoding scheme that assigns a global signature to each pedestrian image and thus, simplifies the matching process. The main contribution of this paper is the design of an extended weighted version of the traditional Fisher vector (FV) encoding scheme. This is achieved by incorporating the Topological location of the encoded descriptors CN, CHS and 15-d in the encoding process and then combining the obtained Topological weighted histograms in order to form our proposed descriptor. The super Fisher vector representation has improved both the rate and the speedup of the person matching process, while weighting the FV encoding scheme by the Topological weight helped out to remove the noisy and busy background clutters surrounding the pedestrians in the images. Besides, Retinex transform was applied in order to handle the problem of illumination variations. Experimental results made on three challenging datasets, the VIPeR dataset, the CUHK03 dataset and the Market-1501 dataset, prove the effectiveness of the proposed method.