Optimized learning rate for energy waste minimization in a background subtraction based surveillance system

In this paper surveillance system employing background subtraction scheme is discussed. The aim of the work is to minimize the waste energy of the overall system due to false positives. Pixels in the foreground of a motion detection system remain non-zero even if a moving object has stopped due to the settling time associated with an adaptive background subtraction scheme as Mixture of Gaussians. Temporal variance in a visually static pixel region also triggers false positives. Optimal learning rate for different parameters as threshold values, ROI size and total number of frames in the scene has been derived in this paper.