Human Pose Detection System Using Machine Learning

Authors

  • Pratiksha Daphal Vishwakarma Institute of Information Technology, Pune – 411048, India
  • Srushti Pokale Vishwakarma Institute of Information Technology, Pune – 411048, India
  • Pranali Chavhan Vishwakarma Institute of Information Technology, Pune – 411048, India
  • Namrata Wasatkar Vishwakarma Institute of Information Technology, Pune – 411048, India
  • Snehal Rathi Vishwakarma Institute of Information Technology, Pune – 411048, India
  • Yashwant Dongre Vishwakarma Institute of Information Technology, Pune – 411048, India
  • Vikas Kolekar Vishwakarma Institute of Information Technology, Pune – 411048, India

Keywords:

Human pose detection, deep learning, machine learning, Human pose estimation, python, CNN, DNN

Abstract

Human pose detection is one of the essential factors in many surveillance-based applications such as fall detection, human-computer interaction activities, sports and fitness, motion or movement analysis, robotics, and many other 'Artificial Intelligence projects and applications. In this survey, we aim to cover the methods that are used before, for human pose detection- single person or multiple people, and examine their efficiency using required parameters, and their real-time compatibility. We will compare and discuss the different methods and technologies used for posture detection and their results. This research can be used to improve the results of systems that use pose detection as their primary parameter, hence can be very helpful for many life-saving applications such as fall detection. We also aim to use this research to develop an efficient model for human pose detection using deep neural networks. The model works on both images and video Human pose detection is one of the essential factors in many surveillance-based applications such as fall detection, human-computer interaction activities, sports and fitness, motion or movement analysis, robotics, and many other 'Artificial Intelligence projects and applications. In this survey, we aim to cover the methods that are used before, for human pose detection- single person or multiple people, and examine their efficiency using required parameters, and their real-time compatibility. We will compare and discuss the different methods and technologies used for posture detection and their results. This research can be used to improve the results of systems that use pose detection as their primary parameter, hence can be very helpful for many life-saving applications such as fall detection.

We also aim to use this research to develop an efficient model for human pose detection using deep neural networks. The model works on both images and videos. The model is built for single-person pose estimation with the help of Machine Learning.

The model is built for single-person pose estimation with the help of Machine Learning.

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Published

16.07.2023

How to Cite

Daphal, P. ., Pokale, S. ., Chavhan, P. ., Wasatkar, N. ., Rathi, S. ., Dongre, Y. ., & Kolekar, V. . (2023). Human Pose Detection System Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 553–561. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3208

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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