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Background Subtraction using Incremental Subspace Learning | IEEE Conference Publication | IEEE Xplore

Background Subtraction using Incremental Subspace Learning


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 tr...Show More

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.
Date of Conference: 16 September 2007 - 19 October 2007
Date Added to IEEE Xplore: 12 November 2007
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Conference Location: San Antonio, TX

References

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