Background Modeling by Combining Joint Intensity Histogram with Time-sequential Data

In this paper, a method for detecting changes from time-sequential images of outside scenes which are taken with several minutes interval is proposed. Recently, statistical background intensity model per pixel using Gaussian mixture model (GMM) has shown its effectiveness for detecting changes from video streams. However, when the time interval between consecutive images is long, enough number of frames can not be sampled for building useful GMM. To robustly build a pixel wise background model at time t₀ from small number of fore and aft frames, we propose to use the joint intensity histogram of the images at time t₀ and t₀ + 1, H(I_t0, I_to + 1). Under “background dominance” condition, background probability distribution for each intensity level at t₀ can be estimated from H(I_t0, I_to + 1). By taking this background probability distribution per intensity as a prior probability, GMM which models the variation in each pixel is robustly calculated even from several frames. Experimental results using actual field monitoring images have shown the advantage of the proposed method.