Support Vector Machine Based Human Detection under Complex Background
基于支持向量机的复杂背景下的人体检测

In the field of computer vision, the research on human detection has a wide application prospect. Prevalent human detection methods usually use traditional statistical theory, which is based on empirical risk minimization. But the minimization of empirical risk over limited training data does not imply good generalization to novel test data. Aiming at the shortcomings of traditional statistical theory used in human detection, a new method based on SVM is presented in this paper. An adaptive background subtraction method combined with color is used to segment the motion objects. Then the trained SVM classifier distinguishes the motion object whether it is a human or not. In order to simplify the design of SVM classifier and improve efficiency of machine learning, a center radiating vector representation is proposed to abstract features of the object. And the optimal representation is obtained by experiments. During the machine learning, a bootstrap method is adopted to reduce the complexity of training SVM. Experiments show that the performance of SVM is better than ANN, and the radial basis function SVM has better performance than other SVMs on human distinguish. This method has strong robustness and high accuracy.