Pedestrian detection method based on wavelet fractal characteristic

Abstract

The invention discloses a pedestrian detection method based on wavelet fractal characteristic, which relates to the technology of intelligent vehicles in an intelligent traffic system. The pedestrian detection method comprises the following steps: (1) reading a training sample set and standardizing all sample images to the pixel size of 48*96; (2) carrying three times of two-dimensional wavelet transformation on the sample images, and getting six wavelet sub-graphs in the second layer and the third layer; (3) getting absolute values of the wavelet sub-graphs obtained in the step (2), and carrying out stretching and scaling so that the numeric area is uniformly mapped to 0 to 255; (4) respectively getting fractal dimension vectors of the wavelet sub-graphs treated in the step (3); (5) standardizing the fractal vectors corresponding to each sub-graph obtained in the step (4); (6) combining the fractal vectors standardized in the step (5), and obtaining the wavelet fractal characteristic vector of 6*(n-1) dimension; and (7) using the wavelet fractal characteristic obtained in the step (6) for training a soft-supporter vector machine, and using the obtained discriminant function for realizing pedestrian detection. The invention has the characteristics of concise characteristic expression form, strong characteristic distinguishing capability and higher pedestrian detection efficiency.

Description

Technical field [0001] The invention relates to an intelligent vehicle technology in an intelligent transportation system, in particular to a pedestrian detection method based on wavelet fractal features. Background technique [0002] Object detection is an important part of machine vision research, and it is widely used in video surveillance, mobile robots, intelligent vehicles and other fields. Due to the ever-changing combination of pedestrian appearance, pose and light intensity, pedestrian detection has become one of the most complex problems in visual object detection. [0003] Existing pedestrian detection methods can be summarized in two main processing steps, feature extraction and object detection. The main task of the target detection step is to use the target features constructed in the feature extraction step for classifier training, so as to make a judgment on new test samples. Therefore, the feature extraction method has an important influence on the effect ...

AI Technical Summary

Problems solved by technology

The target features extracted by these extraction methods contain a large amount of information, which is conducive to classifier training, but the dimension of the feature vector is too high

Taking the HOG method as an example, an image with a size of 64×128 will generate a feature vector of at least 4096 dimensions. If the Haar feature is used, more feature dimensions will be obtained, resulting in large feature redundancy and low computational efficiency. Generally, feature vectors are required. Matching of selection methods

Images