Depth combined structuring and structuring learning method for mankind behavior identification

Abstract

A depth combined structuring and structuring learning method for mankind behavior identification comprises the following steps: 1, forming combined structure and structure formulation; 2, using a space network to extract deep convolution nerve network characteristics from a human body area in an image, using fc6 layer output of the space network as depth characteristics, using gradient histogram and optical flow histogram characteristics to further enhance characteristic expression, connecting CNN, HOG and HOF characteristics so as to express personal behavior or interaction relations in the image, using said characteristics to train two linearity support vector machine classifiers for each data set, and using combined characteristics to calculate combined characteristics in the formula (1); 3, training model parameters; 4, training and related inference in prediction, aiming at each training case to solve loss and enhance inference in each iteration period of the training process. The method is suitable for images of various behavior types, and can identify interaction behaviors.

Description

technical field [0001] The invention belongs to the field of behavior recognition in computer vision, and relates to a human behavior recognition method. Background technique [0002] Recognizing human actions in images or videos is a fundamental problem in computer vision, which is crucial in many applications such as motion video analysis, surveillance systems, and video retrieval. In recent work, deep learning has significantly improved the performance of action recognition. However, these works are not suitable for dealing with data containing multi-person interactions. First, they focus on assigning each image an action label, which is not suitable for images containing multiple action categories. Second, they ignore that the interrelationships between people provide important contextual information for recognizing complex human activities like handshakes, fights, and football games. Contents of the invention [0003] In order to overcome the shortcomings of the ex...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of the existing human behavior recognition methods that cannot be applied to images of multiple behavior categories and cannot identify interactive behaviors, the present invention provides an image suitable for multiple behavior categories and capable of recognizing interactive behaviors for human beings. Deep Joint Structured and Structured Learning Approaches for Action Recognition

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