Robust crowd segmentation and counting in indoor scenes

Abstract

This paper proposes a fast counting approach to estimate the number of people in indoor scenes. Firstly, a pre-processing step is used. In order to obtain a robust gray image in complex light conditions this step includes color correlation, image smoothing and contrast stretch. Secondly, we extract foreground region by background edge modeling and contour filling. Finally, after a foreground normalization based on camera calibration, we obtain the counting results with template matching. Experimental results show that compared with the Bayesian counting approach [2], our approach is robust to illumination variation and achieves a real-time counting result in indoor scenes.