Authors:
Yoshihisa Ijiri
1
;
Shihong Lao
2
;
Tony X. Han
3
and
Hiroshi Murase
4
Affiliations:
1
OMRON Corp., Japan
;
2
OMRON Social Solutions Co. Ltd., Japan
;
3
Univ. of Missouri, United States
;
4
Nagoya Univ., Japan
Keyword(s):
Human Re-identification, Distance Metric Learning, Jensen-Shannon Kernel.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
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.