Crowd Density Classification Based on Confidence Analysis
Wen Ma · 2011
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.