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.
