Moving Object Detection against Sudden Illumination Change Using Improved Background Modeling

Abstract

This paper presents a moving object detection method against sudden illumination change using improved background modeling. Initially, background model is created for every pixel from the first frame. The sample values for the model of a pixel are collected from the neighborhood of that pixel. Then the new pixel values from the new frames are compared to make background foreground decision. Conventional background modeling faces problem with change in the illumination of the scene. The proposed method frequently checks whether abrupt change of illumination take place or not and then initialize the background model from the frame that is detected with changed illumination. The illumination change is detected by obtaining the images of two frames that are taken at a suitable interval in HSV color space. Then the mean change value of each channel is calculated to make a decision. This enables the background model to start over with new sample values that are obtained in the current illumination condition and the background subtraction process can successfully detect moving object with greater accuracy even in changing illumination condition. Simulation results indicate that the proposed method gives excellent results in illumination changing condition to detect moving object whereas the conventional background modeling can not detect accurately. Comparison analysis shows that our proposed method outperforms recent methods in terms of detection accuracy.