Crowd density estimation via Markov Random Field (MRF)
Jinnian Guo, Xinyu Wu, Tian Cao, Shiqi Yu, Yangsheng Xu · 2010
Crowd density estimation is of importance in security monitoring. Many crowd disasters happened because of the loss of control of the crowd density. This paper presents an algorithm to estimate crowd density by employing Markov Random Field (MRF). Three types of image features are extracted for estimating, and they are affected more by the neighboring features than by others, meeting the properties of Markov. The method of least squares is applied to estimate the model of crowd density. The system is applied for real-time videos. The proposed algorithm can estimate the number of people in crowds, and the experiments have shown the effectiveness.