Multiple features based shared models for background subtraction
Yingying Chen; Jinqiao Wang; Hanqing Lu
2015-09
会议名称International Conference on Image Processing (ICIP)
会议录名称International Conference on Image Processing
会议日期2015-9
会议地点Canada
摘要Background 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.
关键词Background Modeling
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12452
专题模式识别国家重点实验室_图像与视频分析
通讯作者Jinqiao Wang
作者单位National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Yingying Chen,Jinqiao Wang,Hanqing Lu. Multiple features based shared models for background subtraction[C],2015.
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