A new Wronskian change detection model based codebook background subtraction for visual surveillance applications

Background subtraction (BS) is a popular approach for detecting moving objects in video sequences for visual surveillance applications. In this paper, a new multi-channel and multi-resolution Wronskian change detection model (MCMRWM) based codebook background subtraction is proposed for moving object detection in the presence of dynamic background conditio ns. In the prooed MCMRWM, the multi-channel information helps to reduce the false negative of the foreground object; and the multi-resolution data suppresses the background noise resulting in reduced false positives. The proposed algorithm considers the ratio between feature vectors of current frame to the background model or its reciprocal in an adaptive manner, depending on the l2 norm of the feature vector, which helps to detect the foreground object completely without any false negatives. Extensive experiments are carried out with challenging video sequences to show the efficacy of the proposed algorithm against state-of-the-art BS techniques.