Fixed-position human behavior analysis method

Abstract

The invention discloses a fixed-position human behavior analysis method, and the method comprises the following steps: 1), carrying out the HOG feature extraction of a human body detection target or scanning window; 2), carrying out the CSS feature extraction of the human body detection target or scanning window; 3), extracting the HOG and CSS features, inputting a trained SVM, and judging whether the inputted trained SVM is a human body or not: carrying out SVM human body detection if the inputted trained SVM is the human body; 4), carrying out the CNN human body second confirmation of a detected human body image; 5), marking key parts in the human body image with landmark key points, connecting the landmark key point, forming integrated description of the human body or face, and carrying out shape regression; 6), carrying out the MHCRF behavior recognition of the series of obtained landmark key points related with a target attitude through the relative position of the obtained landmark key points. The method has the advantages of invariance and normalization, and is especially suitable for the detection of the human body in the image.

Description

technical field [0001] The invention belongs to the technical field of human behavior analysis methods, in particular to a fixed-position human behavior analysis method. Background technique [0002] At present, there are mainly four types of problems in fixed-position human behavior recognition in the world that are difficult to solve: 1. Too much variation between classes within an action class; 2. Multi-view and occlusion problems; 3. Difficulty obtaining training data; 4. Real-time algorithm. In order to solve the above problems, we have developed a set of fixed-position human behavior analysis methods based on the HAR_plus algorithm based on the current international excellent algorithms. Contents of the invention [0003] The purpose of the present invention is to overcome the deficiencies in the prior art, and provide a fixed-position human behavior analysis method with the characteristics of normalization and invariance. [0004] The object of the present inventio...

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Problems solved by technology

[0002] At present, there are mainly four types of problems in fixed-position human behavior recognition in the world that are difficult to solve: 1. Too much variation between classes within an action class; 2. Multi-view and occlusion problems; 3. Difficulty obtaining training data; 4. Real-time algorithm

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