scholarly journals Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Ju
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Recognition Rate ◽  
Linear Representation ◽  
Random Projection ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Human Motion Recognition

Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.

A Projection-based Human Motion Recognition Algorithm based on Depth Sensors

2021 ◽  
pp. 1-1
Author(s):  
Mu-Chun Su ◽  
Pang-Ti Tai ◽  
Jieh-Haur Chen ◽  
Yi-Zeng Hsieh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Depth Sensors ◽  
Human Motion Recognition

scholarly journals Human motion recognition based on limit learning machine

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142093307
Author(s):  
Hong Chen ◽  
Hongdong Zhao ◽  
Baoqiang Qi ◽  
Shi Wang ◽  
Nan Shen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Motion Capture ◽  
Recognition Rate ◽  
Human Motion ◽  
Motion Capture Data ◽  
Joint Position ◽  
Data Set ◽  
Motion Recognition ◽  
Human Motion Recognition ◽  
Learning Machine

With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

Research on End-to-end Voiceprint Recognition Model Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Hong Zhao ◽  
Lupeng Yue ◽  
Weijie Wang ◽  
Zeng Xiangyan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Speech Signal ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Recognition Model ◽  
One Dimensional ◽  
End To End ◽  
Original Speech ◽  
The One

Speech signal is a time-varying signal, which is greatly affected by individual and environment. In order to improve the end-to-end voice print recognition rate, it is necessary to preprocess the original speech signal to some extent. An end-to-end voiceprint recognition algorithm based on convolutional neural network is proposed. In this algorithm, the convolution and down-sampling of convolutional neural network are used to preprocess the speech signals in end-to-end voiceprint recognition. The one-dimensional and two-dimensional convolution operations were established to extract the characteristic parameters of Meier frequency cepstrum coefficient from the preprocessed signals, and the classical universal background model was used to model the recognition model of voice print. In this study, the principle of end-to-end voiceprint recognition was firstly analyzed, and the process of end-to-end voice print recognition, end-to-end voice print recognition features and Res-FD-CNN network structure were studied. Then the convolutional neural network recognition model was constructed, and the data were preprocessed to form the convolutional layer in frequency domain and the algorithm was tested.

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