| Paper: | WA-L2.4 |
| Session: | Video Object Segmentation and Tracking II |
| Time: | Wednesday, September 19, 10:50 - 11:10 |
| Presentation: |
Lecture
|
| Title: |
BACKGROUND SUBTRACTION USING INCREMENTAL SUBSPACE LEARNING |
| Authors: |
Lu Wang; Tsinghua University | | |
| | Lei Wang; Tsinghua University | | |
| | Ming Wen; Tsinghua University | | |
| | Qing Zhuo; Tsinghua University | | |
| | Wenyuan Wang; Tsinghua University | | |
| 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 demonstrate 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. |
