Human Re-identification through Distance Metric Learning based on Jensen-Shannon Kernel

Human re-identification, i. e., human identification across cameras without an overlapping view, has important applications in video surveillance. The problem is very challenging due to color and illumination variations among cameras as well as the pose variations of people. Assuming that the color of human clothing does not change quickly, previous work relied on color histogram matching of clothing. However, naive color histogram matching across camera network is not robust enough for human re-identification. Therefore, we learned an optimal distance metric between color histograms using a training dataset. The Jensen-Shannon kernel is proposed to learn nonlinear distance metrics. The effectiveness of the proposed method is validated by experimental results.