Free-scene egocentric-vision finger key point detection method based on depth convolution nerve network

Abstract

The invention discloses a free-scene egocentric-vision finger key point detection method based on a depth convolution nerve network. The method comprises following steps of S1: obtaining training data, obtaining a region including a hand via proper positioning technology and manually marking coordinates where finger key points locates, wherein the finger key points include finger tips and finger joint pints; S2: designing the depth convolution nerve network and by using the depth convolution nerve network, solving a point coordinate regression problem; S3: training weighing parameters of the depth convolution nerve network via a large number of marking samplings, and after the weighing parameters become stable via certain iteration, obtaining multiple layer of convolution kernel parameters; and S4: using any one foreground picture as input and precisely obtaining finger key point coordinates after calculation of network parameters. The detection method is quite high in precision and good in robustness.

Description

technical field [0001] The present invention relates to the research fields of computer vision and machine learning, in particular to a method for detecting finger key points from the first perspective of a free scene based on a deep convolutional neural network. Background technique [0002] In recent years, with the rise of smart glasses, EgocentricVision gesture interaction technology has attracted widespread attention from academia and industry, especially the emergence of smart wearable devices such as Google Glass and Microsoft Hololens, and virtual reality devices such as Oculus. Human-computer interaction methods are difficult to apply, and an algorithm is urgently needed to help devices understand human interaction needs, such as gesture operations. Gesture interaction technology mainly involves two aspects, gesture recognition and key point positioning. The present invention focuses on key point positioning, namely fingertip detection and positioning and knuckle de...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, provide a free scene first-view finger key point detection method based on a deep convolutional neural network, solve the problem of finger key point detection in static images, and then apply it to video Finger keypoint recognition and tracking in streams

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