Adaptive background estimation of outdoor illumination variations for foreground detection

A background estimation system, which integrates pixel-level features with a region-level one and combines short-term and long-term analysis of videos in outdoor illumination variations, is proposed for accurate foreground detection. Firstly, we discuss autocorrelation-based features for identification of the presence of foreground and outdoor illumination variations in short-term sequences, and propose an adaptive threshold learning approach insensitive to inner-pixel fast illumination variation based on histograms of intensity differences between successive frames. Then, we employ a pixel-wise rapid autoregressive model against gradual illumination change for background estimation in long-term sequence. Finally, we devise a texture measure to eliminate the regional effect of fast illumination variation. The effectiveness of our system is demonstrated using experiments on foreground detection in videos with various illumination changes.