Multiple features based shared models for background subtraction | |
Yingying Chen![]() ![]() ![]() | |
2015-09 | |
会议日期 | 2015-9 |
会议地点 | Canada |
关键词 | Background Modeling |
英文摘要 | 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. |
会议录 | International Conference on Image Processing
![]() |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/12452] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
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]. 见:. Canada. 2015-9. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论