轮廓特征与神经网络相结合的行人检测  

Human Detection Based on Contour Features and Neural Networks

作　　者：刘琳[1] 耿俊梅[2] 顾国华[1] 钱惟贤[1] 徐富元[1] 

机构地区：[1]南京理工大学电子工程与光电技术学院,南京210094 [2]济源职业技术学院,河南济源454650

出　　处：《光电工程》2014年第7期50-56,共7页Opto-Electronic Engineering

基　　金：教育部新世纪优秀人才(NCET-12-0630);国家自然科学基金(61271332)

摘　　要：传统的基于方向梯度直方图与支持向量机的行人检测方法运算量大,针对这一问题,本文从轮廓特征的角度出发,提出了头肩轮廓特征与神经网络相结合的检测方法。该方法根据人体头肩模型具有相对稳定性,且轮廓特征可以作为人体识别的依据,采用边缘检测与均值漂移相结合的方式提取人体轮廓,采用经PCA降维的傅里叶描述子提取轮廓特征,结合神经网络分类器完成初次人体识别。采用RGB头发模型和均值漂移方法,对遮挡情况下被判别为非人体的目标图像做进一步处理,聚类出多个人体头肩模型,重新参与分类。实验结果表明,本方法人体检测的准确率和检测速度与现有的算法相比都有所提高,且克服了遮挡情况下人体头肩模型提取错误的弊端,提高了人体检测的识别率和应用范围。The traditional method based on the histogram of oriented gradients and Support Vector Machine causes large amount of computation. To deal with the problem, a novel method called the contour feature of head-shoulders combined with neural network is proposed. The head-shoulder model is relatively stable and the contour feature can be used as a basis for human identification. There are two main parts in the paper. Firstly, the head-shoulder model was extracted by edge detection and mean shift algorithm. Then Fourier descriptors with PCA dimensionality reduction were calculated according to contours of the head-shoulder model. Combined with neural network classifier, the initial human identification was completed. Secondly, several models of human head-shoulders from aim pictures which have been identified as non-person with RGB hair mode and the mean-shift algorithm were clustered and classified them again. The experiment result shows that, the detection accuracy and speed are improved compared with the conventional algorithms, and it performances well when shelters occur.

关 键 词：均值漂移 头肩轮廓提取 PCA傅里叶描述子 神经网络 人体检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]