Threshold Modeling of Basic Statistical Methods for Effective Motion Detection in Video Surveillance

This paper empirical investigates two basic statistical methods namely Adaptive Median and Adaptive Mean for motion detection in video surveillance for the optimization of parameters namely threshold and the refresh rate of background frame used in these methods. Experimentation shows that the optimum choice of parameters majorly affects the quality of motion detection. The performance of methods for different parameters is measured using precision, recall  and f1-score. PR curves are also drawn which are based on precision and recall values to show the effect of different parameters. Test data includes six data sets from different scenarios of ‘CDNet2012’. Experimental results verify that for every method there are fixed values of parameters with slight variations which gives better result of object motion. These parameter values can be used or adapted for future experimentation on these methods with respect to each scenario.