基于深信度网络分类算法的行人检测方法

针对目前浅层分类方法存在训练样本数量过大和拟合复杂函数能力较弱等不足,提出一种改进的基于深信度网络分类算法的行人检测方法。首先,通过搭建带T分布函数显层节点的受限波兹曼机输入端改进深信度网络的输入方式,将行人特征提取信息通过输入端的显层结构转化为分类器可以识别的伯努利分布方式；其次,搭建多隐层受限波兹曼机中间层结构,实现隐层结构间的数据传递,保留关键信息。最后,利用BP神经网络搭建分类结构的输出端,实现分类误差信息反向传播并对分类结构的参数进行微调,不断优化分类器结构。实验证明,改进的深信度网络行人检测算法性能优于经典浅层分类算法,算法的检测速度也能满足使用要求。

Pedestrian Detection Method Based on Deep Belief Network Classification Algorithm

In view of the problem that the training sample size is large and the complex function fitting ability is weak in shallow classification method, proposed a pedestrian detection method based on improved deep belief network classification algorithm. Firstly, RBM with T distribution function show layer nodes built an improve way of input, which could change information of pedestrian feature to Bernoulli distribution and recognized Bernoulli distribution; in addition, set up the middle layer RBM structure, which achieve data transfer between the hidden layer structure, and keep the key information. Finally, paper used the BP neural network for output of classifier, which could back propagation the information of misclassification, and minor adjustments parameters of classification structure. Experimental results show that the improved deep belief network pedestrian detection method is better than other shallow classic algorithms, and the real time also can meet the needs of practical use.