CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Multiple features based shared models for background subtraction
Yingying Chen; Jinqiao Wang; Hanqing Lu
Conference NameInternational Conference on Image Processing (ICIP)
Source PublicationInternational Conference on Image Processing
Conference Date2015-9
Conference PlaceCanada
AbstractBackground modeling is a fundamental problem in computer vision and usually as the first step for high-level applications. Pixel based approaches usually ignore the spatial coherence, while region based approaches are sensitive to region size and scene complexity. In this paper, we propose a robust background subtraction approach via multiple features based shared models. Each shared model is represented by a sequence of samples based on sample consensus. Each pixel dynamically searches a matched model around the neighborhood. This shared mechanism not only enhances the robustness for background noise and jitter but also significantly reduces the number of models and samples for each model. Besides, we concatenate color and texture features as multiple features according to the discriminability and complementarity, so that each pixel can find a proper model more easily. Finally, the shared models are updated by random selecting a pixel matched the model with an adaptive update rate. Experiments on ChangeDetection benchmark 2014 show that the proposed approach outperforms the state-of-the-art methods.
KeywordBackground Modeling
Document Type会议论文
Corresponding AuthorJinqiao Wang
AffiliationNational Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yingying Chen,Jinqiao Wang,Hanqing Lu. Multiple features based shared models for background subtraction[C],2015.
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