Single-shot person re-identification by Gaussian mixture model of weighted color histograms

In video surveillance, person re-identification is an important task of recognizing individuals in diverse locations over different non-overlapping camera views under the condition of large illumination variations. To deal with these challenges, an efficient appearance-based-method was proposed for the single-shot person re-identification, that use a mixture of Gaussian models to weight HSV color histograms as color features. The proposed approach has been tested on a public benchmark dataset, VIPeR, for evaluation. The experimental results demonstrate superior recognition rate and execution performance by using the proposed method compared to the other representative methods.