A Probabilistic Framework Based on KDE-GMM hybrid model (KGHM) for Moving Object Segmentation in Dynamic Scenes
文献类型：会议论文
| 作者 | Zhou Liu; Wei Chen; Kaiqi Huang ; Tieniu Tan
|
| 出版日期 | 2008 |
| 会议日期 | 2008 |
| 会议地点 | Marseille , France |
| 关键词 | Kde-gmm Hybrid Model |
| 页码 | 1-8 |
| 英文摘要 | In real scenes, dynamic background and moving cast shadow always make accurate moving object detection difficult. In this paper, a probabilistic framework for moving object segmentation in dynamic scenes is proposed. Under this framework, we deal with foreground detection and shadow removal simultaneously by constructing probability density functions (PDFs) of moving objects and non-moving objects. Here, these PDFs are constructed based on KDEGMMhybrid model (KGHM) which has advantages of KDE and GMM. This KGHM models the spatial dependencies of neighboring pixel colors to deal with highly dynamic scenes. Moreover, in this framework, tracking information is used to refine the PDF of moving objects. Experimental results demonstrate the effectiveness of our method. |
| 会议录 | IEEE Conference on Computer Vision & Pattern Recognition 2008
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12708]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Kaiqi Huang |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Zhou Liu,Wei Chen,Kaiqi Huang,et al. A Probabilistic Framework Based on KDE-GMM hybrid model (KGHM) for Moving Object Segmentation in Dynamic Scenes[C]. 见:. Marseille , France. 2008. |
入库方式： OAI收割
来源：自动化研究所
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