Optimal wavelet differencing method for robust motion detection

In real-world surveillance systems, where variation of light and camera parameters can sometimes severely impair the normal operation of background subtraction algorithms, better results are obtained with differencing schemes. We have earlier demonstrated that differencing of detail images produced by wavelet transformation can lead to more stable detection results. In this paper, we considerably extend that framework, by introducing the modified z-scores calculated from wavelet coefficient differences. Foreground pixels are detected as outliers in normal distribution by modified z-score test. The threshold value used in the outlier test is optimized by maximizing the precision and recall measures on several training frames. Finally, the elimination of ghosts from motion detection is done by double modified z-score testing, that is similar in idea to double frame differencing. The resulting motion detection method shows considerable resilience to changes in illumination and camera parameters and also produces a lower amount of detection errors than other motion detection methods.