Pedestrian identification method based on gradient cascade SVM (Support Vector Machine) classifier

A pedestrian recognition and classifier technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of slow nonlinear SVM, unfavorable for fast recognition, and impact on SVM classification performance.

Inactive Publication Date: 2017-05-31
INST OF INFORMATION ENG CAS
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Problems solved by technology

The SVM classifier has unique advantages for nonlinear small sample and high-dimensional data classification, but the training of SVM requires a large amount of storage space, which will affect the performance of the SVM classifier when there are a large number of non-pedestrian targets in the detected picture
In addition, for the case of linear inseparability, SVM uses a kernel function to map the input space to the feature space for solution,

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  • Pedestrian identification method based on gradient cascade SVM (Support Vector Machine) classifier
  • Pedestrian identification method based on gradient cascade SVM (Support Vector Machine) classifier
  • Pedestrian identification method based on gradient cascade SVM (Support Vector Machine) classifier

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[0033] The traditional method of pedestrian detection based on HOG features and SVM classifier is slow in detection speed and not strong in real-time, and is not suitable for scenes with small traffic flow and a large number of non-pedestrian targets. Aiming at this problem, the present invention proposes a pedestrian recognition method based on gradient cascaded SVM classifiers, using risk-sensitive classifiers and voting mechanisms to improve the detection accuracy to a certain extent, and using gradient cascade architecture to speed up the detection stage The classification speed improves the computing efficiency and the overall processing speed. The overall processing flow is as figure 2shown. Among them: classifier 1 is a simple cascaded classifier trained using low-dimensional features, and classifier 2 is a cascaded SVM classifier that uses high- and low-dimensional features to train a gradient from simple to complex (the number of series generally takes classifier 1 ...

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Abstract

The invention discloses a pedestrian identification method based on a gradient cascade SVM (Support Vector Machine) classifier. In the method, pedestrian identification is carried out by use of a risk-sensitive classifier and a voting mechanism, the detection precision is improved to a certain degree; and furthermore, a gradient cascade architecture is judged by use of an assembly line type from coarse grains to fine grains, negative samples which are easily judged can be completely detected in first levels, the classification speed in the detection phase is greatly accelerated, the operation efficiency and the total processing speed are improved. The method can be adapted to pedestrian identification in scenes with a small pedestrian flow and a lot of non-pedestrian targets.

Description

technical field [0001] The invention belongs to the technical field of video monitoring, and relates to an intelligent video monitoring method, in particular to a pedestrian recognition method based on a gradient cascaded SVM classifier, and is especially suitable for pedestrians in a scene where there is less pedestrian traffic and a large number of non-pedestrian areas exist identify. Background technique [0002] Head and shoulders recognition is a kind of pedestrian recognition method, by detecting the head and shoulders features of pedestrians in the video to determine whether they are pedestrians. Relying on image processing technology, this technology is mainly implemented based on pedestrian feature extraction and recognition algorithms. The purpose is to realize the recognition of pedestrians passing through a specific area by processing pedestrian information in video streams. It is an intelligent video surveillance system. an important branch of . So far, the ma...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/103G06V10/50G06F18/2411
Inventor 孙利民田莹莹芦翔文辉张园
Owner INST OF INFORMATION ENG CAS
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