Optimization of person re-identification through visual descriptors

Person re-identification is a complex computer vision task which provides authorities a valuable tool for maintaining high level security. In surveillance applications, human appearance is considered critical since it possesses high discriminating power. Many re-identification algorithms have been introduced that employ a combination of visual features which solve one particular challenge of re-identification. This paper presents a new type of feature descriptor which incorporates multiple recently introduced visual feature representations such as Gaussian of Gaussian (GOG) andWeighted Histograms of Overlapping Stripes (WHOS) latest version into a single descriptor. Both these feature types demonstrate complementary properties that creates greater overall robustness to re-identification challenges such as variations in lighting, pose, background etc. The new descriptor is evaluated on several benchmark datasets such as VIPeR, CAVIAR4REID, GRID, 3DPeS, iLIDS, ETHZ1 and PRID450 s and compared with several state-of-the-art methods to demonstrate effectiveness of the proposed approach.