Pedestrian detection method on basis of sparse representation

Abstract

The invention discloses a pedestrian detection method on the basis of a sparse representation. The method comprises two phases of model training and comparison identification. On the two phases, the operations of respectively carrying out normalization processing on a training image and a detection image, extracting three feature vectors of each image, i.e. color, texture and shape, carrying out sparse representation on the three feature vectors of each image and combining the three feature vectors of each image into a sparsification mixed feature vector are carried out; on the phase of model training, according to the sparsification mixed feature vector of the training image, a classifier is trained by a model training method of a support vector machine; and on the phase of comparison identification, according to the sparsification mixed feature vector of the detection image, identification is carried out by the classifier. The method has excellent detection performance, has a better effect on more data sets and also has good robustness on detection of a shielded difficult image.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a pedestrian detection method based on sparse representation. Background technique [0002] Pedestrian detection has broad application prospects in video control, robotics, intelligent transportation, multimedia retrieval and other fields, and it is also a popular research object in the field of computer vision in recent years. However, factors such as pedestrian clothing, body posture, viewing angle, complex background and lighting changes will affect the detection effect, which is the difficulty of pedestrian detection. [0003] Pedestrian detection can be regarded as a pedestrian / non-pedestrian classification problem, and its current classification methods can be divided into two categories: [0004] (1) Classification based on shape information. Including methods based on display human body models, methods based on template matching and methods based...

AI Technical Summary

Problems solved by technology

This type of method refers to the detection of pedestrians by analyzing their gait (Gait) features when they are moving. Its advantage is that it is not affected by changes in texture and light. The disadvantage is that it can only recognize moving pedestrians and requires multiple frames to give a judgment. As a result, the real-time performance of the system is affected

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