A method for counting moving and stationary people by interest point classification
Chi Yoon Jeong, Su-Gil Choi, Seung Wan Han · 2013
While the methods for people counting based on moving interest points have been shown good performance, the problem related to count static or temporarily stopped people remains particularly challenging. This paper presents a novel method for people counting which is considering moving and stationary people. The proposed method first separates the moving and static points by the motion information. Then, the temporarily static points of stationary people classified by analyzing the texture information of the points between the current and the eigenbackground image. Finally, we estimate the number of people using the moving and the temporarily static points. The experimental results show that the proposed method can identify the static points related to people correctly. Additionally, the results confirm that the proposed method reduces the estimation error in the sequences containing the many temporarily static people.