High efficient moving object extraction and classification in traffic video surveillance |
| |
Authors: | Li Zhihua Zhou Fan Tian Xiang Chen Yaowu |
| |
Affiliation: | Inst.of Advanced Digital Technology and Instrument,Zhejiang Univ.,Hangzhou 310027,P.R.China |
| |
Abstract: | Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method. |
| |
Keywords: | background model nonparametric model adaptive single Ganssian model object classification |
本文献已被 维普 万方数据 等数据库收录! |
| 点击此处可从《系统工程与电子技术(英文版)》浏览原始摘要信息 |
|
点击此处可从《系统工程与电子技术(英文版)》下载免费的PDF全文 |