基于稀疏表达和机器学习的行人检测技术研究

Study on Pedestrian Detection Based on Sparse Representation and Machine Learning

针对行人检测技术在智能交通系统中的应用,为了提高行人检测方法的有效性、实时性和准确性,将稀疏表达应用到图像的特征压缩中,提出一种基于HOG和LTP特征训练SVM分类器进行行人检测的方法。基于HOG和LTP特征训练SVM分类器进行行人检测的方法有效地结合了图像的梯度特征和纹理特征,利用稀疏表达进行特征数据的压缩可以有效地加速算法。实验结果表明,提出的算法具有精度高、速度快等优点。

According to the application of pedestrian detection technology in the intelligent transportation system,in order to improve the efficiency,real-time and accuracy of pedestrian detection method,in this paper,the sparse representation was applied to the feature compression of the image,and a new method of pedestrian detection based on HOG and LTP feature training SVM classifier was proposed.Training SVM classifier for pedestrian detection based on the characteristics of HOG and LTP effectively combines the image gradient feature and texture features and takes advantage of the sparse expression on data compression which can effectively speed up the algorithm.Experimental results show that the proposed algorithm has the advantages of high precision and speed.