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.