基于SURF的高密度人群计数方法

摘要

为了解决在高密度人流或视场开阔环境下人群计数准确率低的问题,提出一种基于SURF的高密度人群计数方法.首先采用最小生成树改进了传统的基于密度的聚类算法,使其最小搜索域自适应聚类数据的分布；在此基础上实现运动人群的SURF特征点分类,并以此构建运动人群的特征向量,用支持向量回归机实现了对高密度人群的数量统计.实验结果表明,该方法对高密度人群的计数有较高的准确率和鲁棒性.%This paper presents a SURF-based method for high-density crowd counting, focusing on overlaying the low counting accuracy in a high-density crowd or open environment. The traditional density-based clustering algorithm (DBSCAN) by adopting minimum spanning tree (MST) is improved, making its minimal search domain adaptive to the distribution of clustering data. Then the SURF features of moving crowd through the improved DBSCAN algorithm is classified. An eigenvector which can represent the moving crowd is built on the clustering results. Finally, the number of crowd through a support vector regressor (e-SVR) is got. The experimental results confirm that the proposed method have a high accuracy and robustness to the high-density crowd counting.