基于深信度网络分类算法的行人检测方法.

In view of the problem that the training sample size is large and the complex function fitting ability is weak in shallow classification method, this paper 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 achieved data transfer between the hidden layer structure, and kept the key information. Finally, this 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. [ABSTRACT FROM AUTHOR]