A real-time people counting approach in indoor environment

Abstract

Due to complex background information, shadow and occlusions, it is difficult to count people accurately. In this paper, we propose a fast and robust human counting approach in indoor space. Firstly, we use foreground object extraction to remove background information. In order to get both moving people and stationary people, we designed a block-updating way to update the background model. Secondly, we train a multi-view head-shoulder model to find candidate people, and an improved k-means clustering is proposed to locate the position of each people. Finally, a temporal filter with frame-difference is used to refine the counting results and detect noise, such as double-count, random disturbance. An indoor people dataset is recorded in the classroom of our university. Experiments and comparison show the promise of the proposed approach.