Yoshihisa Ijiri, Shihong Lao, Tony X. Han, Hiroshi Murase


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.


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Paper Citation

in Harvard Style

Ijiri Y., Lao S., X. Han T. and Murase H. (2012). HUMAN RE-IDENTIFICATION THROUGH DISTANCE METRIC LEARNING BASED ON JENSEN-SHANNON KERNEL . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 603-612. DOI: 10.5220/0003850506030612

in Bibtex Style

author={Yoshihisa Ijiri and Shihong Lao and Tony X. Han and Hiroshi Murase},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
SN - 978-989-8565-03-7
AU - Ijiri Y.
AU - Lao S.
AU - X. Han T.
AU - Murase H.
PY - 2012
SP - 603
EP - 612
DO - 10.5220/0003850506030612