Learning from mistakes: object movement classification by the boosted features

Authors:
Shigeyuki Odashima;Tomomasa Sato;Taketoshi Mori
Affiliations:
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

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Abstract

This paper proposes a robust object movement detection method via a classifier trained by mis-detection samples. The mis-detection are related to the environment, such as reflection on a display or small movement of a curtain, so learning the patterns of mis-detections will improve the detection precision. The mis-detections are expected to have several features, but selecting manually optimal features and thresholds is difficult. In order to acquire optimal classifier automatically, we employ a ensemble learning framework. The experiment shows the method can detect object movements sufficiently by constructing the classifier automatically by the proposed framework.