A Novel Method to Improve Basic Background Subtraction Methods for Object Detection in Video Surveillance System

摘要

This paper proposes a novel method for the improvement of basic Background Subtraction (BGS) methods to detect moving objects in video surveillance streams. The method is based on Local Neighborhood Differencing (LND) in which instead of finding a simple pixel to pixel difference between current frame and background model, the average of the pixel neighborhoods from the current frame and background model are subtracted to entitle the pixel a background or foreground in the current frame in order to find moving objects in video. The proposed method has been tested on two basic methods; Adaptive Mean and Adaptive Median methods of object detection using various complex real time benchmarked scenarios. It is also compared with classical statistical thresholding method. The results have been measured in precision and recall metrics to register improvement. The obtained results have confirmed the utility of the method by increasing the robustness of the object detection techniques in video surveillance for real time video analytic.