ACCV 2010: Computer Vision – ACCV 2010 Workshops pp 318-327 | Cite as
Learning from Mistakes: Object Movement Classification by the Boosted Features
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.
Keywords
Object Movement Color Histogram Stable Change Object Candidate Object Movement DetectionPreview
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