针对行人检测在实时和准确率方面的要求,提出基于优化核函数支持向量机的行人检测方法,以梯度方向直方图算法提取行人特征,以支持向量机算法作为分类器。在传统算法的基础上,提出以组合核函数作为分类器核函数,并设置松弛变量,引入惩罚因子,结合遗传算法与K重交叉验证进行组合系数和参数的优化与选择,根据优化后的参数构成最终分类器进行行人检测。其检测达到较好效果,满足对实时性和准确性的要求。

According to the requirements of real-time and accuracy of people detection, we propose the support vector machine based on optimized kernel function in people detection, which uses histogram of oriented gradients algorithm to extract the features of people and the support vector machine algorithm as the classifier. On the basis of the traditional algorithm, we propose the combined kernel function as the kernel function of the classifier. After setting the slack variable and introducing the penalty factor, we combine genetic algorithm and K-fold cross validation optimization to select and optimize the combination coefficients and parameters, and build the final classifier for people detection based on the optimize parameters. Results show that the proposed algorithm achieves better result, and can satisfy the requirement of real-time and accuracy in people detection.