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Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
March 2013 (vol. 35 no. 3)
pp. 597-610
Xiaowei Zhou, Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Can Yang, Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Weichuan Yu, Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
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.
Index Terms:
Motion segmentation,Object detection,Cameras,Computer vision,Estimation,Computational modeling,Hidden Markov models,motion segmentation,Moving object detection,low-rank modeling,Markov Random Fields
Citation:
Xiaowei Zhou, Can Yang, Weichuan Yu, "Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 597-610, March 2013, doi:10.1109/TPAMI.2012.132
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