基于置信度分析的人群密度等级分类模型

人群密度等级估计是智能人群监控的核心技术之一。其主要应用是统计监控图像或视频中指定监控区域内的人群密度量化等级。文中提出一种基于置信度分析的人群密度等级分类模型。首先设计基于二叉树分类思想的纠错输出编码，优化组合多个二分类器。然后提取置信样本，训练SVM二分类器。最后利用信道传输模型进行解码，依据后验概率最大法则得到样本所属的人群密度等级。该模型在样本集和特征相同的前提下分类正确率和泛化性能均优于传统分类模型，为以人群密度估计为代表的多类分类问题求解提供一种思路。

Crowd Density Classification Based on Confidence Analysis

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.