Crowd Density Classification Based on Confidence Analysis
MA Wen-Hua 1,2, HUANG Lei2, LIU Chang-Ping2
1.Hanvon Technology Laboratory, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 2.Graduate School of Chinese Academy of Sciences, Beijing 100190
Abstract Crowd density estimation is crucial for crowd monitoring and is mainly used for calculating quantified levels for crowd density of target monitor areas in videos or images. A crowd density classifier is proposed based on confidence analysis. Several binary classifiers are firstly combined together by error correcting output codes, which is designed under the guidance of binary tree theory. Confidence samples are selected and used for training support vector machines, which are adopted as binary classifiers. The decoding algorithm is based on transmission channel model and the samples are assigned to classes with maximum posterior probabilities. Experimental results demonstrate that the proposed approach is superior to the traditional classification models under the premise of same dataset and features, which provides a method for multi-category classification such as crowd density estimation.
[1] Velastin S A, Yin J H, Davies A C, et al. Analysis of Crowd Movements and Densities in Built-up Environments Using Image Processing // Proc of the IEE Colloquium on Image Processing for Transport Applications. London, UK, 1993: 1-6 [2] Kong Dan, Gray D, Tao Hai. A Viewpoint Invariant Approach for Crowd Counting // Proc of the 18th IEEE International Conference on Pattern Recognition. Hongkong, China, 2006, Ⅲ: 1187-1190 [3] Marana A N, da Costa L, Lotufo R A, et al. On the Efficacy of Texture Analysis for Crowd Monitoring // Proc of the International Symposium on Computer Graphics, Image Processing, and Vision. Impa, Brazil, 1998: 354-361 [4] Rahmalan H, Nixon M S, Carter J N. On Crowd Density Estimation for Surveillance // Proc of the IEEE International Conference on Crime Detection and Prevention. London, UK, 2006: 540-545 [5] Ma Wenhua, Huang Lei, Liu Changping. Advanced Local Binary Pattern Descriptors for Crowd Estimation // Proc of the IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application. Wuhan, China, 2008, Ⅱ: 958-962 [6] Allwein E L, Shapire R E, Singer Y. Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning Research, 2001, 1: 113-141 [7] Fei B, Liu J. Binary Tree of SVM: A New Fast Multiclass Training and Classification Algorithm. IEEE Trans on Neural Networks, 2006, 17(3): 696-704 [8] Pujol O, Radeva P. Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 28(6): 1007-1012 [9] Hsu C W, Lin C J. A Comparison of Methods for Multi-Class Support Vector Machines. IEEE Trans on Neural Networks, 2002, 13(2): 415-425 [10] Ma R, Li L, Huang W, et al. On Pixel Count Based Crowd Density Estimation for Visual Surveillance // Proc of the IEEE Conference on Cybernetics and Intelligent Systems. Singapore, Singapore, 2004: 170-173 [11] Marana A N, da Costa L, Lotufo R A, et al. Estimating Crowd Density with Minkowski Fractal Dimension // Proc of the International Conference on Acoustics, Speech and Signal Processing. Phoenix, USA, 1999, Ⅵ: 3521-3524 [12] Wu Xinyu, Liang Guoyuan, Lee K K, et al. Crowd Density Estimation Using Texture Analysis and Learning // Proc of the IEEE International Conference on Robotics and Biomimetics. Kunming, China, 2006: 214-219 [13] Xu Yong, Lu Guangming. Analysis on Fisher Discriminant Criterion and Linear Separability of Feature Space // Proc of the IEEE International Conference on Computational Intelligence and Security. Hongkong, China, 2006: 1671-1676 [14] Tao Qin, Wu Gaowei, Wang Feiyue, et al. Posterior Probability Support Vector Machines for Unbalanced Data. IEEE Trans on Neural Networks, 2005, 16(6): 1561-1573 [15] Takenouchi T, Ishii S. Multiclass Classification as a Decoding Problem // Proc of the IEEE Symposium on Foundations of Computational Intelligence. Honolulu, USA, 2007: 470-475
2011, Vol. 24 
