Title :
Crowd Density Estimation Using Texture Analysis and Learning
Author :
Wu, Xinyu ; Liang, Guoyuan ; Lee, Ka Keung ; Xu, Yangsheng
Author_Institution :
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin
Abstract :
This paper presents an automatic method to detect abnormal crowd density by using texture analysis and learning, which is very important for the intelligent surveillance system in public places. By using the perspective projection model, a series of multi-resolution image cells are generated to make better density estimation in the crowded scene. The cell size is normalized to obtain a uniform representation of texture features. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris-Laplacian space is also applied. The texture feature vectors are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem of calculating the crowd density. Finally, based on the estimated density vectors, the SVM method is used again to solve the classification problem of detecting abnormal density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.
Keywords :
image resolution; image texture; regression analysis; support vector machines; traffic engineering computing; Harris-Laplacian space; abnormal density distribution; crowd density estimation; intelligent surveillance system; multi-resolution image cells; perspective projection model; real crowd videos; regression problem; support vector machine; texture analysis; texture feature measurements; Disaster management; Image edge detection; Image segmentation; Image texture analysis; Machine learning; Magnetic heads; Support vector machine classification; Support vector machines; Surveillance; Videos; Abnormal detection; Crowd density; Machine learning; Surveillance; Texture analysis;
Conference_Titel :
Robotics and Biomimetics, 2006. ROBIO '06. IEEE International Conference on
Conference_Location :
Kunming
Print_ISBN :
1-4244-0570-X
Electronic_ISBN :
1-4244-0571-8
DOI :
10.1109/ROBIO.2006.340379