Hand posture tracking method, device and system

Abstract

The invention provides a hand posture tracking method, device and system. The hand posture tracking method comprises the steps: obtaining a depth image comprising a hand region; and obtaining a targethand posture through a target network model according to the depth image, wherein the target network model is a learning model which is trained in advance and is used for carrying out feature extraction and feature recognition on the depth image to obtain a target initial hand posture and optimizing the target initial hand posture to output the target initial hand posture, so that the accuracy and high efficiency of posture tracking are improved. Real-time hand posture tracking can be achieved without the need for high computing device resources (such as a GPU (Graphics Processing Unit)).

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a method, device and system for hand posture tracking. Background technique [0002] With the popularization of depth sensors and the demand in the field of human-computer interaction, hand gesture recognition and tracking based on depth data is one of the indispensable and important ways in new human-computer interaction technology, and has a wide range of application prospects, such as gesture remote control wireless Man-machine, gesture-controlled household robots, somatosensory games and assisted medical operations, etc. Compared with the traditional hand pose estimation based on RGB image, the depth data can provide the three-dimensional distance information of the hand. [0003] The existing hand gesture recognition in human-computer interaction requires the user's hand to be parallel to the imaging plane of the camera. However, people's hand gestures often...

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Problems solved by technology

[0003] The existing hand gesture recognition in human-computer interaction requires the user's hand to be parallel to the imaging plane of the camera. However, people's hand gestures often form a certain angle with the horizontal plane, which makes hand gesture recognition and tracking accuracy not high.

[0004] Or most of the existing hand tracking methods use fixed hand models. Different users have hands of different sizes. Using a fixed template hand will reduce the tracking accuracy. Sometimes it is necessary to pre-calibrate the hand shape according to different users, and the operation is cumbersome. , the attitude tracking efficiency is low, reducing the user experience

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