A motion recognition algorithm using polytopic modeling

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.

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Research on motion recognition algorithm based on bag-of-words model

Microsystem Technologies ◽

10.1007/s00542-019-04462-8 ◽

2019 ◽

Author(s):

Ting Huang ◽

Sheng-Rong Ru ◽

Zhi-Hong Zeng ◽

Long Zhang

Keyword(s):

Recognition Algorithm ◽

Bag Of Words ◽

Motion Recognition

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A Study on Hand Motion Recognition Algorithm using Time-of-Flight Camera

The Korean Data Analysis Society ◽

10.37727/jkdas.2017.19.1.217 ◽

2017 ◽

Vol 19 (1) ◽

pp. 217-228

Author(s):

Kisang Yoo ◽

Seohoon Jin ◽

Sejung Jang

Keyword(s):

Time Of Flight ◽

Recognition Algorithm ◽

Motion Recognition ◽

Hand Motion

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Human motion recognition based on SVM in VR art media interaction environment

Human-centric Computing and Information Sciences ◽

10.1186/s13673-019-0203-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 7

Author(s):

Fuquan Zhang ◽

Tsu-Yang Wu ◽

Jeng-Shyang Pan ◽

Gangyi Ding ◽

Zuoyong Li

Keyword(s):

Genetic Algorithm ◽

Dimensional Space ◽

Dimensional Subspace ◽

Recognition Algorithm ◽

Human Motion ◽

Support Vector ◽

Motion Recognition ◽

Linear Discriminant ◽

Classification Feature ◽

Human Motion Recognition

AbstractIn order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.

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A Novel Pedestrian Dead Reckoning Solution Using Motion Recognition Algorithm with Wearable EMG Sensors

Journal of Global Positioning Systems ◽

10.5081/jgps.10.1.39 ◽

2011 ◽

Vol 10 (1) ◽

pp. 39-49 ◽

Cited By ~ 6

Author(s):

Qian Wang ◽

Yuwei Chen ◽

Xiang Chen ◽

Xu Zhang ◽

Ruizhi Chen ◽

...

Keyword(s):

Dead Reckoning ◽

Recognition Algorithm ◽

Motion Recognition ◽

Pedestrian Dead Reckoning

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Intelligent recognition of dance training movements based on machine learning and embedded system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189954 ◽

2021 ◽

pp. 1-13

Author(s):

Dixin Zhang

Keyword(s):

Real Time ◽

Smart Home ◽

Three Dimensional ◽

Recognition Algorithm ◽

Human Motion ◽

Support Vector ◽

Motion Recognition ◽

Motion Data ◽

Local Database ◽

Error Recognition

Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.

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