3D (three-dimensional) convolutional neural network based human body behavior recognition method

Abstract

The present invention discloses a 3D (three-dimensional) convolutional neural network based human body behavior recognition method, which is mainly used for solving the problem of recognition of a specific human body behavior in the fields of computer vision and pattern recognition. The implementation steps of the method are as follows: (1) carrying out video input; (2) carrying out preprocessing to obtain a training sample set and a test sample set; (3) constructing a 3D convolutional neural network; (4) extracting a feature vector; (5) performing classification training; and (6) outputting a test result. According to the 3D convolutional neural network based human body behavior recognition method disclosed by the present invention, human body detection and movement estimation are implemented by using an optical flow method, and a moving object can be detected without knowing any information of a scenario. The method has more significant performance when an input of a network is a multi-dimensional image, and enables an image to be directly used as the input of the network, so that a complex feature extraction and data reconstruction process in a conventional recognition algorithm is avoided, and recognition of a human body behavior is more accurate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a human behavior recognition method based on a 3D convolutional neural network in the technical field of computer vision. The invention can be used in an intelligent monitoring system to identify abnormal behaviors of human bodies in the environment, and can also be used in sports training to regulate the actions of athletes. Background technique [0002] Previous human action recognition methods are based on some harsh assumptions about the application scenarios, such as small scale changes and small visual changes of the target. But this is difficult to satisfy in the real world. [0003] Currently, in this regard, most current methods follow two steps: (1) extract complex artificial features in the original input; (2) learn a classifier on the acquired features. [0004] In real-world scenarios, it is almost difficult to know what features are important for a ...

AI Technical Summary

Problems solved by technology

The present invention solves the recognition problem of human behavior by adopting 3D convolutional neural network technology

Using 3D Convolutional Neural Network Models for Motion Recognition

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