Passenger detecting method based on Haar-PCA characteristic and probability neural network

Abstract

The invention discloses a passenger detecting method based on a Haar-PCA characteristic and a probability neural network. The passenger detecting method comprises the steps of manually selecting a large number of passenger pictures and non-passenger pictures, and respectively marking the passenger pictures and the non-passenger pictures as positive samples and negative samples; representing a random positive sample or negative sample Si by means of an Haar characteristic, generating an Haar characteristic vector Hi, wherein i=1, 2, ..., n; selecting a main element subset vector Hi<PCA> which comprises majority part information on the Haar characteristic vector Hi of all samples through main component analysis; determining a classifier CPNN structure and determining to-be-classified samples, similarly with the step 2 and step 3, representing a to-be-determined sample according to the Harr-PCA characteristic vector Hi<PCA>, and inputting the vector into the CPNN which is obtained in the step 4, wherein the to-be-determined sample belongs to a class of which the output neuron is 1. The passenger detecting method has advantages of greatly reducing number of dimensions of a training characteristic vector through the Haar-PCA characteristic, reducing training load of the classifier, and greatly improving detection accuracy through replacing traditional BPANN by means of the probability neural network (PNN).

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image information perception, in particular to a pedestrian detection method based on Haar-PCA features and a probability neural network. Background technique [0002] Traffic safety has become a major worldwide issue, and the impact of automobile safety on human life and property is self-evident. With the development of highways and the improvement of automobile performance, the speed of automobiles has also increased accordingly. In addition to the increase in the number of automobiles and the increasingly busy traffic, the casualties and property losses caused by the increase in automobile accidents have become a social problem that cannot be ignored. The driving safety of automobiles is even more important. Traditional passive safety is far from being able to avoid traffic accidents, but active safety technology has attracted much attention because it can prevent accid...

AI Technical Summary

Problems solved by technology

However, BPANN has two disadvantages: 1. The training is easy to fall into the local minimum point and reduce the detection rate; 2. BPANN needs to determine the network weight through training.

When the number of samples is large and the dimensionality is large, the BPANN training time is lengthy, requiring dozens of hours or even days

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