Human body detecting method based on SURF (Speed Up Robust Feature) efficient matching kernel

Abstract

The invention provides a human body detecting method based on an SURF efficient matching kernel, and mainly solves the problem that image background hybridity can not be better processed in the existing method. The method comprises the steps that a negative sample is obtained through bootstrap in an INRIA (Institute National de Recherce en Informatique et Automatique) database, and a training sample set of the whole human body is formed by the negative sample and a positive sample in the database; SURF descriptor feature points are extracted under different image scales for the training sample; feature points are extracted by random sampling to constitute the initial vector basis of a visual vocabulary; constrained singular value decomposition is utilized for the initial vector basis to obtain the maximum kernel function feature; the maximum kernel function feature in different image scales is weighted to obtain the features under all the image scales; the obtained features are trained in different classes by an SVM (Support Vector Machine) classifier, and a detection classifier is obtained; and the image to be detected is input to the classifier to obtain the final detection result. The method disclosed by the invention can be used for accurately detecting the human body, and can be used for intelligent monitoring, driver auxiliary systems and virtual video.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a static human body detection method, and can be used for intelligent monitoring, driver assistance systems, human body motion capture, pornographic image filtering and virtual video. Background technique [0002] In the field of computer vision, human body detection is a technology with very broad application prospects. Various factors such as occlusion make human detection a very difficult problem. At present, the methods of human detection in static images mainly include detection methods based on motion characteristics, methods based on human body models and methods based on statistical classification. [0003] The detection method based on motion characteristics is to use the posture change of the human body when it is stable and the symmetry of the human body to change periodically, construct a self-similar matrix in the time domain, and reflect the difference from othe...

AI Technical Summary

Problems solved by technology

The advantage of the method based on statistical classification is that the detection results are stable and the effect is good. The disadvantage is that it requires a lot of training data, and it is difficult to solve the problems of insufficient light and background confusion.

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