Multi-layer feature histogram with correlative degree for cross-camera-based person re-identification

Person re-identification via cross-camera is a difficult problem in the field of target discovery and tracking. Traditional solutions depending on the characteristics of a target's appearance have low reliability and can easily lead to low matching rate because they use simple metric functions. This work proposes a more reliable measurement: correlative degree of a target's features among different camera views to do person re-identification and uses it to measure the histograms' similarity of targets. In order to obtain more discriminative features, we need to extract their appearance and space features. We use multi-layer histograms to describe them. In order to compute more accurate correlation degree, we propose to use Gaussian pyramid as alternating distance to define high-dimensional diffusion distance. Finally, we assign different weights to feature vectors so as to establish the correlative degree function based on diffusion distance. Experiments of person re-identification for different cameras show that the proposed method can achieve much better results than some known existing methods.