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UAV (Unmanned Aerial Vehicle) man-machine interaction method based on binocular vision and deep learning

A deep learning and binocular vision technology, applied in neural learning methods, input/output of user/computer interaction, computer components, etc. Solve the effects of camera drift and complex background interference, wide working range and high accuracy

Inactive Publication Date: 2017-04-26
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Traditional action recognition algorithms have high computational complexity, and due to the lack of necessary acceleration algorithms, the recognition speed is slow and the accuracy is low

Method used

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  • UAV (Unmanned Aerial Vehicle) man-machine interaction method based on binocular vision and deep learning
  • UAV (Unmanned Aerial Vehicle) man-machine interaction method based on binocular vision and deep learning
  • UAV (Unmanned Aerial Vehicle) man-machine interaction method based on binocular vision and deep learning

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Embodiment Construction

[0032] The present invention will be further described in detail below in conjunction with the accompanying drawings and through specific embodiments. The following embodiments are only descriptive, not restrictive, and cannot limit the protection scope of the present invention.

[0033] A human-computer interaction method for drones based on binocular vision and deep learning, the specific steps are as follows:

[0034] 1) When the system is started, according to the content displayed by the camera, the navigator is framed from a single point of view displayed on the ground station, and the navigator is tracked by using the fast tracking algorithm. Low-resolution video sequence in the center.

[0035] 2) Using the stereo matching algorithm based on block matching to perform stereo matching on the low-resolution part, this part of the stereo matching is accelerated by the graphics processor. At the same time, the parameters of this step provide the maximum and minimum paralla...

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Abstract

The invention relates to a UAV (Unmanned Aerial Vehicle) man-machine interaction method based on binocular vision and deep learning. An embedded image processing platform and a binocular camera are carried on a UAV. The embedded image processing platform is connected to a flight controller through an interface. The UAV communicates with a ground station through the platform. The platform is provided with a Graphics Processing Unit (GPU), a convolutional neural network deep learning algorithm is run and parallel computing for an image is carried out during video capture of the camera. According to the invention, a convolutional neutral network is transplanted to the embedded platform with a dedicated GPU and the run speed becomes faster through parallel computing for speeding up the computation.

Description

technical field [0001] The method belongs to the field of multimedia information processing, and specifically relates to computer vision, deep learning, human-computer interaction and other technologies, especially a method for human-computer interaction of drones based on binocular vision and deep learning. Background technique [0002] Human-computer interaction technology was born with the birth of computers and gradually developed with the development of computer hardware and software. The emergence of new technologies has continuously simplified the process of human-computer interaction. In recent years, with the emergence and development of artificial intelligence technology and the continuous progress and innovation of related software and hardware technologies, how to achieve more convenient human-computer interaction has become a research hotspot, and various new human-computer interaction technologies are emerging. At the same time, with the rise and popularization...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F3/01G06N3/08G06T7/11G06T7/194G06T7/254G06K9/00
CPCG06F3/017G06N3/08G06T2207/10016G06V40/23
Inventor 侯永宏叶秀峰侯春萍刘春源陈艳芳
Owner TIANJIN UNIV
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