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Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

Authors Zhou, Xiaowei HKUST affiliated (currently or previously)
Yang, Can View this author's profile
Yu, Weichuan View this author's profile
Issue Date 2013
Source IEEE Transactions on Pattern Analysis and Machine Intelligence , v. 35, (3), March 2013, article number 6216381, p. 597-610
Summary Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually. performed by object detectors or background subtraction techniques. Often, an object detector requires Manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based Methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named, DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.
ISSN 0162-8828
Language English
Format Article
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