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Published in:
2011 | OriginalPaper | Chapter
Learning from Mistakes: Object Movement Classification by the Boosted Features
Authors : Shigeyuki Odashima, Tomomasa Sato, Taketoshi Mori
Published in: Computer Vision – ACCV 2010 Workshops
Publisher: Springer Berlin Heidelberg
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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.