Adaptive real-time motion segmentation technique based on statistical background model

Abstract

Motion segmentation is a crucial step for video analysis and has many applications. This paper proposes a method for motion segmentation, which is based on construction of statistical background model. Variance and Covariance of pixels are computed to construct the model for scene background. We perform average frame differencing with this model to extract the objects of interest from the video frames. Morphological operations are used to smooth the object segmentation results. The proposed technique is adaptive to the dynamically changing background because of change in the lighting conditions and in scene background. The method has the capability to relearn the background to adapt these variations. The immediate advantage of the proposed method is its high processing speed of 30 frames per second on large sized (high resolution) videos. We compared the proposed method with other five popular methods of object segmentation in order to prove the effectiveness of the proposed technique. Experimental results demonstrate the novelty of the proposed method in terms of various performance parameters. The method can segment the video stream in real-time, when background changes, lighting conditions vary, and even in the presence of clutter and occlusion

Keywords:

• Motion segmentation
• Background learning
• Statistical model
• Real-time video processing

Acknowledgements

This work was supported in part by the Department of Science and Technology, New Delhi, India, under grant no. SR/FTP/ETA-023/2009, and the University Grants Commission, New Delhi, India, under grant no. 36–246/2008(SR).