¹Beijing Key Laboratory of Information Service Engineer,Beijing Union University, Beijing, China
²Information Technology College, Beijing Union University, Beijing, China

---

Abstract—In this paper, we propose a method to estimate crowd density using improved Harris and Optics Algorithms. We pre-processed the raw images at first and the corner features of the crowd were detected by the improved Harris algorithm, then the formed density point data were used to analyze the corner characters of crowd density by the optics density clustering theory. This theory is related to the distribution of the feature points where the crowd density is estimated by the machine learning algorithm.We used a standard database PETS2009 to do the experiments in this paper and the self-shooting datasets to illustrate the effectiveness of our method. The proposed approach has been tested on a number of image sequences. The results show that our approach is superior to other methods including the original Harris algorithm. Our method improves the efficiency of estimation and has a significant impact on preventing the accidents on crowd area with high density.

Index Terms—density clustering, Harris algorithm, OPTICS algorithm, crowd density

[PDF]

Cite: Cheng Xu, Hong Bao, Lulu Zhang, Ning He, "Crowd Density Estimation based on Improved Harris & OPTICS Algorithm," Journal of Computers vol. 9, no. 5, pp. 1209-1217, 2014.