Convolutional neural network model for static image behavior recognition

A convolutional neural network and static image technology, applied in the field of convolutional neural network models, can solve problems such as failure, and achieve the effect of preventing overfitting problems

Pending Publication Date: 2020-02-04
JIANGXI UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Convolutional Neural Networks (CNNs) is the mainstream classification model in the current image recognition field, but a large number of human behavior categories in real life have static attributes, which makes video-based human behavior recognition technology in this type of human behavior Failed in identification

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  • Convolutional neural network model for static image behavior recognition
  • Convolutional neural network model for static image behavior recognition
  • Convolutional neural network model for static image behavior recognition

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Embodiment Construction

[0021] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0022] In the following description, a lot of specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, and those skilled in the art can do it without departing from the meaning of the present invention. By analogy, the present invention is therefore not limited to the specific examples disclosed below.

[0023] Such as figure ...

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Abstract

The invention discloses a convolutional neural network model for static image behavior recognition, and relates to the technical field of image processing methods. The model comprises a VGG16 convolution layer module used for processing an input image; a multi-branch convolution layer module used for respectively learning features output by the last layer of the VGG16 convolution layer module through different branches, wherein the convolution layer weight of the module is randomly initial in the training process; and a softmax classifier layer module used for classifying the features outputby the three-branch convolution layer module. The model can explore information in a VGG16 convolutional layer module channel more effectively, and can play a role in finely adjusting the highest layer weight in the neural network; therefore, the action information in the single static human behavior image can be identified more effectively.

Description

technical field [0001] The invention relates to the technical field of image processing methods, in particular to a convolutional neural network model for static image behavior recognition. Background technique [0002] Human action recognition is one of the important research contents in the field of computer vision. Most of the research on human behavior recognition is based on video rather than single image methods, but there are many common human behaviors that can be fully represented by a single image, for example, phone calls, computer interaction, shooting Wait. Even if the video information of these actions is available, methods based on static cues are still required, such as playing guitar, riding a horse, running, etc., that is to say, the range of motion of these human actions is small, and the movement trajectory is not discriminative, so it is still necessary to recognize these actions. Static method based on a single image. [0003] Algorithms based on dee...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/10G06N3/045G06F18/241
Inventor 于祥春张哲吴垒庞巍陈贺昌于哲舟李斌
Owner JIANGXI UNIV OF SCI & TECH
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