Human Behavior Feature Representation and Recognition Based on Depth Video
DOI:
https://doi.org/10.13052/jwe1540-9589.195614Keywords:
Artificial Intelligence, Composite Features of Video Images, Motion Recognition, Feature Extraction AlgorithmAbstract
With the continuous development of computer artificial intelligence technology, various applications based on artificial intelligence emerge in an endless stream, among which video image recognition technology is the most widely used in life. This article starts from the process of image recognition, based on the composite characteristics of artificial intelligence and video images, to discuss human gesture recognition technology.This article uses the feature extraction algorithm for image composite feature extraction as a method, and conducts human body movement collection experiments, analyzes the database and The gesture recognition step. This paper mainly introduces the extraction method of image composite features and the basic requirements of gesture recognition, and through the algorithm calculation of feature extraction, the function of human gesture recognition video and image composite features is completed, and the human action collection experiment is carried out to confirm. The results of images and data show the advantages of the algorithm support used in this article. We will Dmti. MsHOG is compared with other methods in the three subsets. In terms of the accuracy of all tests, our method performs better than other methods. The results show that the MSHOG (Multi-scale Histogram of Oriented Gradients) descriptor can represent the unique characteristics of human behavior, reflecting the effectiveness of our proposed method. In particular, this method achieved 100% recognition accuracy in Test, with an average recognition accuracy of 94.91%, which is significantly better than existing methods.
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Y. Lü, Zhao J, Cao F. Image Denoising Algorithm Based on Composite Convolutional Neural Network. Moshi Shibie Yu Rengong Zhineng/pattern Recognition & Artificial Intelligence, 2017, 30(2):97-105.
Nguyen H T, Nguyen L T, Dreglea A I. Robust approach to detection of bubbles based on images analysis. International Journal of Artificial Intelligence, 2018, 16(1):167-177.
CACM Staff. Artificial Intelligence. Communications of the ACM, 2017, 60(2):10-11.
Y. Ming, G. Wang, X. Hong. Spatial-temporal texture features for 3D human activity recognition using laser-based RGB-D videos. ksii transactions on internet & information systems, 2017, 11(3):1595-1613.
Hassabis D, Kumaran D, Summerfield C, et al. Neuroscience-Inspired Artificial Intelligence. Neuron, 2017, 95(2):245-258.
Ma J, Yu J, Hao G, et al. Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model. Lipids in Health and Disease, 2017, 16(1):1-7.
Xie T, Qin P, Yan J. Research on Artificial Intelligence Frontier Recognition Based on LDA. Open Access Library Journal, 2018, 05(12):1-13.
Han M. Application of Artificial Intelligence Detection System Based on Multi-sensor Data Fusion. International Journal of Online Engineering (iJOE), 2018, 14(6):31.
Li M, f financial auditing teaching mode based on artificial intelligence expert system. Zhang H, Chen B, et al. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods. Scientific Reports, 2018, 8(1):3991.
Xinman Z. Construction oBoletin Tecnico/Technical Bulletin, 2017, 55(17):743-747.
Wang Y, Duan H. Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information. Remote Sensing, 2018, 10(3): 441.
Li H, Luo W, Qiu X, et al. Identification of Various Image Operations Using Residual-Based Features. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(1):31-45.
Latonov V V, Tikhomirov V V. Line-of-Sight Guidance Control Using Video Images. Moscow university mechanics bulletin, 2018, 73(1):11-17.
Latonov V V. Programmed Strategies to Test the Quality of Line-of-Sight Guidance Control Using Video Images. Moscow University Mechanics Bulletin, 2018, 73(6):135-140.
Zhi X, Yan J, Hang Y, et al. Realization of CUDA-based real-time registration and target localization for high-resolution video images. Journal of Real Time Image Processing, 2019, 16(4):1025-1036.
Gurov I P , Volkov M V , Margaryants N B , et al. Method of bringing locally varying images into coincidence in video capillaroscopy. Journal of Optical Technology c/c of Opticheskii Zhurnal, 2019, 86(12):774.
Liu L , Liu G , Chu X M , et al. Ship Detection and Tracking in Nighttime Video Images Based on the Method of LSDT. Journal of Physics Conference Series, 2019, 1187(4):042074.
Itakura K, Hosoi F. Estimation of tree structure parameters from video frames with removal of blurred images using machine learning. Journal of Agricultural Meteorology, 2018, 74(4):154-161.
Chen H, Ye S, Nedzvedz O V, et al. Application of Integral Optical Flow for Determining Crowd Movement from Video Images Obtained Using Video Surveillance Systems. Journal of Applied Spectroscopy, 2018, 85(1):126-133.
Zhang L X. Research on similarity measurement of video motion images based on improved genetic algorithm in paper industry. Paper Asia, 2019, 2(1):135-138.
