Efficient Weighted Histogram Features for Single-Shot Person Re-Identification

Abstract

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. In order to deal with these challenges, two different and efficient color-based-methods are proposed for single-shot person re-identification in this article, which uses the Gaussian mixture model to combine with color histograms (low-level feature) and the dense salient patches (mid-level feature) as the color features. Both the two proposed systems are three-stage processes. The first stage is the image enhancement by illumination normalization, and it used to deal the intensity variations. The second stage includes pedestrian segmentation and human region partition, which separates the background (BG) and foreground (FG) and locates the body segments to improve the accuracy of feature extracting and matching. The third stage is to perform feature extracting and matching. A Gaussian mixture model is used in the first system, GMMWCH, to generate the weighted color histogram as the color features, which has a low computation time and a good recognition rate. For the second system, SaliGMMWCH, the dense correspondence is used to link the color histogram weighted by the Gaussian mixture model to find salient regions. Even though that takes more time for computation, the SaliGMMWCH retains a better recognition rate than GMMWCH. In addition, the correct match can be chosen by matching the similarity scores of different feature with an appropriate weight selection. Both the proposed methods have been tested on the benchmark, VIPeR and PRID 2011, for evaluation. The experimental results demonstrate superior recognition rate and execution performance by using the proposed methods compared to other representative methods.