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A DeskVR-Oriented Gesture Tracking and Recognition Joint Strategy Method

A gesture and strategy technology, applied in the field of human-computer interaction, can solve the problems of occupying GPU computing resources, poor real-time performance, and easy fatigue, and achieve the effect of good recognition effect, high real-time performance, and good speed and accuracy.

Active Publication Date: 2020-08-04
ZHEJIANG UNIV OF TECH
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Current gesture recognition methods still struggle to accurately identify joint positions under large occluded regions
3) Easy fatigue: desktop VR experience requires users to wear VR equipment
Traditional model-based methods have faster speed but lower recognition accuracy for complex gestures
The complex CNN network has high precision but poor real-time performance, occupying a lot of GPU computing resources

Method used

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  • A DeskVR-Oriented Gesture Tracking and Recognition Joint Strategy Method
  • A DeskVR-Oriented Gesture Tracking and Recognition Joint Strategy Method
  • A DeskVR-Oriented Gesture Tracking and Recognition Joint Strategy Method

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Embodiment Construction

[0065] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0066] like figure 1 As shown, a joint strategy method for gesture tracking and recognition for deskVR specifically includes the following steps:

[0067] Training CNN model stage.

[0068] S01: Get the dataset and preprocess it. The public dataset ICVL is used for model training. According to the contour processing function in opencv, detect and draw its outer contour according to the binary image of the hand. According to the Douglas-Pocke algorithm, find the closed polygon of the contour. Determine the center of the closed polygon, and extract a cube with a size of 150mm centered on the center of the area. The cube is then resized to a 96×96 image, and the depth values ​​are normalized to [-1,1] as the input to the ConvNet.

[0069] S02: Construct a convolutional neural network, the specific structure is: a five-region convol...

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Abstract

The invention discloses a joint strategy method for gesture tracking and recognition oriented to deskVR. Firstly, the gesture public data set ICVL is trained offline through a convolutional neural network to obtain a model with good robustness, high accuracy and fast recognition speed, and through synthesis Data test the balance point Rate between the accuracy and speed of the joint method, and then capture the depth image in real time through the depth camera. The regional convolutional neural network model returns the 3D information of the recognized joint points, and then maps the 3D human body model. The identification method of the present invention has good interactive functions in the deskVR application environment, and performs well in terms of recognition rate, operation speed, universality, accuracy and the like.

Description

technical field [0001] The invention belongs to the technical field of human-computer interaction, and in particular relates to a joint strategy method for gesture tracking and recognition facing deskVR. Background technique [0002] The poster child for immersive applications is desktop VR. It can be widely used for data analysis, watching VR movies. These applications typically provide stereo vision and head tracking, while they also need to provide gesture recognition for human-computer interaction. Sitting in a chair and resting your elbows on a table enables long gesture interactions in a VR environment. Because head-mounted displays (HMDs) block the eyes from viewing the real world, mice and keyboards are no longer suitable for immersive virtual environments. Joysticks are the most mature solution at the lowest cost, however the user has no direct control. [0003] Currently, gesture sensors on the market, including Leap Motion, use "HMD + gesture sensor" to achiev...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/20G06N3/045
Inventor 卢书芳蔡历丁雪峰高飞毛家发
Owner ZHEJIANG UNIV OF TECH
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