Background Subtraction in Videos using Bayesian Learning with Motion Information

This paper proposes an accurate and fast background subtraction technique for object tracking in still camera videos. Regions of motion in a frame are first estimated by comparing the current frame to a previous one. A samplingresampling based Bayesian learning technique is then used on the estimated regions to perform background subtraction and accurately determine the exact pixels which correspond to moving objects. An obvious advantage in terms of processing time is gained as the Bayesian learning steps are performed only on the estimated motion regions, which typically constitute only a small fraction of the frame. The technique has been used on a variety of indoor and outdoor sequences, to track both slow and fast moving objects, under different lighting conditions and varying object-background contrast. Results demonstrate that the technique achieves high degrees of sensitivity with considerably lower time complexity as compared to existing techniques based on mixture modeling of the background.