Backward Substraction Algorithm for Stationary and Dynamic Background Video Sequences

Backward Substraction Algorithm for Stationary and Dynamic Background Video Sequences

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Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. Most methods for foreground region detection in videos are challenged by the presence of quasi-stationary backgrounds flickering monitors, waving tree branches, moving water surfaces or rain. Additional difficulties are caused by camera shake or by the presence of moving objects in every image. In this paper, we proposed a background subtraction algorithm based on background reconstruction method for both stationary and dynamic background video sequences. Firstly, pre-processing is used to obtained the appropriate video frames from video sequences. Secondly, background is reconstructed by averaging and filtering method . Finally, the initial video object is derived in each frame by subtracting the background from this image, after that, mathematic morphology post-processing is used to get an accurate video object. Experiments on typical sequences have successfully demonstrated the validity of the proposed algorithm.

Index Terms— Video segmentation, Background reconstruction, Background subtraction, Moving object.
I. INTRODUCTION
Video segmentation refers to the identification of region in a frame of video that are homogeneous tin some sense. Different features and homogeneity criteria generally lead to different segmentation of same data; for example, color segmentation, texture segmentation, and motion segmentation usually result in subdivision maps. Furthermore, there is no assurance that any of the resulting s...


... middle of paper ...


...post processing, there still are a few interior holes, but the background algorithm is enough to meet the need of video surveillance.

IV. CONCLUSION
In this paper, background subtraction algorithm based on background reconstruction is proposed to detect moving objects from video sequences. The segmentation results show that the proposed algorithm can extract moving objects effectively from both static as well as dynamic background. Simple averaging method gives the appropriate results for static background and modified averaging method gives the perfect results for dynamic background. This algorithm is appropriate for application to surveillance-type video. Thus, our future work is mainly focused on extraction of objects from unconstrained videos, for example, video obtained from broadcast news networks or home videos.

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