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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lu Wang Lei Wang Ming Wen Qing Zhuo Wenyuan Wang |
| Copyright Year | 2007 |
| Description | Author affiliation: Tsinghua Univ., Beijing (Lu Wang; Lei Wang; Ming Wen; Qing Zhuo; Wenyuan Wang) |
| Abstract | Background modeling and subtraction is a basic component of many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and activity analysis. In this paper, we propose an effective and adaptive background modeling and subtraction approach that can deal with dynamic scenes. The proposed approach uses a subspace learning method to model the background and the subspace is updated on-line with a sequential Karhunen-Loeve algorithm. A linear prediction model is also used to make the detection more robust. Experimental results show that the proposed approach is able to model the background and detect moving objects under various type of background scenarios with close to real-time performance. |
| File Size | 234675 |
| File Format | |
| ISBN | 9781424414369 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2007.4379761 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Layout Object detection Computer vision Application software Learning systems Predictive models Robustness Video surveillance Principal component analysis Automation Subspace Method Background Subtraction Object Detection |
| Content Type | Text |
| Resource Type | Article |
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