Reducing Run-Time for Moving Object Detection in Background Buffering Approach

An efficient moving object Segmentation is useful for real time content based video surveillance and Object Tracking. Commonly a foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the Object Trackers. This technique uses a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge in the new location of the object. In the background model, the post processing step is applied to the obtained object mask to remove noise region and to smoothen the object boundary which incurs additional delay. In this paper a Background Buffering algorithm (BBA) is proposed to construct a reliable background model based on a sequence of input frames. Except moving object data, the other data is used to build reliable background representation. The result shows significant decrease in run-time for the higher level processing steps of surveillance system.