A video gesture classification method method

A classification method and video technology, applied in the field of deep learning application research, can solve problems such as low recognition rate, and achieve an effect that is conducive to application promotion

Inactive Publication Date: 2019-01-25
XIDIAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the limitations of the existing gesture recognition technology in the real three-dimensional scene, especially the low

Method used

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  • A video gesture classification method method

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Embodiment

[0030] The processing of this embodiment is performed on the Chalearn LAP IsoGD dataset. This dataset was proposed by Wan et al. in the paper "Chalearn looking at people rgb-d isolated and continuous datasets for gesture recognition" on CVPRW2016. The dataset has a total of 47,933 gesture videos, and each video is made by a volunteer. Actions, the data set contains a total of 249 gestures. Among them, 35878 videos are used as training set, 5784 videos are used as verification set, and 6271 videos are used as test set.

[0031] refer to figure 1 , the video gesture classification method of this embodiment includes:

[0032] Step 1, read video data

[0033] Use matlab software to read video data, where the data includes visible light video (RGB video) and depth video (depth video) acquired by sensors such as Kinect.

[0034] Step 2, spatio-temporal normalization of video data

[0035] 2a) Spatial normalization of video data. The present invention normalizes the video in ste...

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Abstract

The invention relates to a video gesture classification method. The method comprises the following steps: the visible light video and the depth video of the same object set are normalized; the saliency of each frame in the visible light video is calculated to obtain the saliency video; the three-dimensional convolution neural network is respectively trained by using normalized visible light video,depth video and salient video, wherein the three-dimensional convolution neural network comprises an input layer, 18 convolution layers, a global average pooling layer and a full connection layer arranged in turn, the features of the global average pooling layer of the spatial dimensions of the visible light video, depth video and salient video are fused to obtain the fused features, and the video gestures are classified according to the obtained fused features. The invention aims at the problem of large-scale gesture recognition based on video, and the category data and the video data amountthereof are far larger than those of the prior invention. The invention can carry out real-time identification on the video, and has more credibility and practicability.

Description

technical field [0001] The invention belongs to the field of deep learning application research, and further relates to a dynamic gesture classification method based on a three-dimensional deep neural network in a convolutional neural network, which can be applied to human-computer interaction, intelligent driving, intelligent wear, game entertainment, etc. place. Background technique [0002] Gestures are also essentially a language system. Gestures can be divided into static gestures and dynamic gestures. Static gestures mainly focus on factors such as the shape, outline, and center of gravity of gestures; dynamic gestures record key points such as the trajectory of the hand and the direction of waving the hand. The corresponding gesture recognition technology is also divided into static recognition technology and dynamic recognition technology. Static gesture mainly refers to the direction of human gestures, the shape and texture of gestures, and is suitable for scenes t...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/28G06F18/2411G06F18/253
Inventor 苗启广李宇楠徐昕戚玉涛房慧娟马振鑫齐相达张鸿远权义宁宋建锋
Owner XIDIAN UNIV
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