Dynamically visual learning for people identification with sparsely distributed cameras

Authors:
Hidenori Tanaka;Itaru Kitahara;Hideo Saito;Hiroshi Murase;Kiyoshi Kogure;Norihiro Hagita
Affiliations:
Intelligent Robotics and Communication Laboratories, ATR, Kyoto, Japan;Intelligent Robotics and Communication Laboratories, ATR, Kyoto, Japan;Graduate School of Science and Technology, Keio University, Yokohama, Japan;Graduate School of Information Science, Nagoya University, Nagoya, Japan;Intelligent Robotics and Communication Laboratories, ATR, Kyoto, Japan;Intelligent Robotics and Communication Laboratories, ATR, Kyoto, Japan
Venue:
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Year:
2005

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Abstract

We propose a dynamic visual learning method that aims to identify people by using sparsely distributed multiple surveillance cameras. In the proposed method, virtual viewpoint images are synthesized by interpolating the sparsely distributed images with a simple 3D shape model of the human head, so that virtual densely distributed multiple images can be obtained. The multiple images generate an initial eigenspace in the initial learning step. In the following additional learning step, other distributed cameras capture additional images that update the eigenspace to improve the recognition performance. The discernment capability for personal identification of the proposed method is demonstrated experimentally.