Knowledge Commons of Institute of Automation,CAS
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 | 紫东太初大模型研究中心_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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Multiple features ba(1729KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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