Re-identification of pedestrians with variable occlusion and scale

This paper presents results from experiments designed to measure the accuracy with which people can be reidentified using multiple visual surveillance observations. Two public data sets are used: VIPeR and a new public data set, V-47. The re-identification method is a Large Margin Nearest Neighbour classifier using feature vectors constructed from overlapping block histograms. The experiments investigate the performance with respect to the level of occlusion, the training regime, specificity of the domain and the resolution of the observations. A method is proposed that reduces the adverse impact of occlusions, when present; and increases the beneficial impact of higher resolution data, when available.