A SURVEY ON HUMAN POSE ESTIMATION USING MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.17605/OSF.IO/KTFUQKeywords:
Human pose estimation, Mechine LearningAbstract
Human posture assessment is a deeply rooted problem in computer vision that has implemented many challenges in the past. Analysis of video surveillance, biometric, human activitices in many fields such as home-assisted, health surveillance can be beneficial.In fast-moving life, people usually prefer exercising at home but they need a instructor to evaluate their exercise form.Human pose recognition can be used to use self-instructional training methods such as watching fitness vidoes, that allows people to learn and train properly. Today in developing countries faced many sensitive issues in public places, so monitoring is manditory. Human pose estimation application like video surveillance system is used for monitoring human activity in public areas like malls, hospitals, beach etc.Many researchers used different application for give best result in human pose technique, in this survey compare whichalgorithm gives best performance in human pose estimation
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Abhay Gupta, Kuldeep Gupta, Kshama Gupta and Kapil Gupta.,“Analyzing student performance using evolutionary artificial neural network algorithm”, International Semantic Intelligence Conference, Vol. 2786, pp. 323-330, 2021.
Steven Chen and Richard R. Yang.,” Performance Pose Trainer: Correcting Exercise Posture using Pose Estimation”,arXiv, cornell university,pp.1-7,2020.
Neelam V. Puri and P. R. Devale.,”Human Tracking and pose Estimation in Video Surveillance System”,International Journal of Computer Science and Engineering (IJCSE),Vol 2(3), pp.107-114, 2013.
Abhay Gupta, Kuldeep Gupta, Kshama Gupta and Kapil Gupta., “Human Activity Recognition Using Pose Estimation and Machine Learning Algorithm”,International Semantic Intelligence Conference, February pp.323–330, 2021, New Delhi, India.
Palanimeera. j, and K. Ponmozhi.,”Analyzing Classification of yoga pose using machine learning techniques”,Elsevier Ltd, pp.1-4,2020.
Ryuzo Okada and Stefano Soatto.,"Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images",European Conference on Computer Vision, Springer, pp.434-445,2008.
Kruti Pandya et al.,”Virtual Coach: Monitoring Exercises and Aerobic Dance Generation”,International Research Journal of Engineering and Technology, Vol 8(5),pp.3588-3593,2021.
Arunnehru.j et al.,”Human Pose Estimation and Activity Classification Using Machine Learning Approach ”,Soft Computing and Signal Processing, pp 113-123, 2020.
Ahmed Taha et al.,”Skeleton-based Human Activity Recognition for Video Surveillance”,International Journal of Scientific & Engineering Research, Vol. 6(1),pp.993-1004,2015.
Ahmed Taha et al.,”Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset”,arxiv.org, Vol. 6(1),pp.1-8,2019.
S. Porwal, S. Singh, N. Yadav and D. Garg, "Review Paper of Human Activity Recognition using Smartphone," 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 939-946, 2021.
Domen Novak et al., “Workload Estimation in Physical Human–Robot Interaction Using Physiological Measurements”,Published by Oxford University Press on behalf of The British Computer Society,pp.1-14,2014.
Dimitrios .I et al.,“Human Behavior Classification using Multiple Views”, Conference: Artificial Intelligence: Theories, Models and Applications, 5th Hellenic Conference on AI, SETN 2008, Syros, Greece, October 2-4, 2008.
Vina Ayumi and Mohamad I. Fanany., “A Comparison of SVM and RVM for Human Action Recognition”,Internetworking Indonesia Journal, Vol. 8(1),pp.29-33,2016.
Sri Harsha, N.C., Anudeep, Y.G.V.S., Vikash, K. et al. Performance Analysis of Machine Learning Algorithms for Smartphone-Based Human Activity Recognition. Wireless Personal Communication, 2021.
Yogameena,B et al.,”Human Behavior Classification Using Multi-Class Relevance Vector Machine”,Journal of Computer Science, Vol. 6 (9).pp.1021-1026, 2010.
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