CAJ | 학술논문

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。
전통적기우방향제도직방도여지지향량궤적행인검측방법운산량대，침대저일문제，본문종륜곽특정적각도출발，제출료두견륜곽특정여신경망락상결합적검측방법。해방법근거인체두견모형구유상대은정성，차륜곽특정가이작위인체식별적의거，채용변연검측여균치표이상결합적방식제취인체륜곽，채용경PCA강유적부리협묘술자제취륜곽특정，결합신경망락분류기완성초차인체식별。채용 RGB 두발모형화균치표이방법，대차당정황하피판별위비인체적목표도상주진일보처리，취류출다개인체두견모형，중신삼여분류。실험결과표명，본방법인체검측적준학솔화검측속도여현유적산법상비도유소제고，차극복료차당정황하인체두견모형제취착오적폐단，제고료인체검측적식별솔화응용범위。
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.