Malik, Najeeb Ur Rehman and Abu Bakar, Syed Abdul Rahman and Sheikh, Usman Ullah (2022) Multiview human action recognition system based on OpenPose and KNN classifier. In: Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications Enhancing Research and Innovation through the Fourth Industrial Revolution. Lecture Notes in Electrical Engineering, 829 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 890-895. ISBN 978-981168128-8
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Official URL: http://dx.doi.org/10.1007/978-981-16-8129-5_136
Abstract
Human action recognition is one of the trending research topics in the field of computer vision. Human-computer interaction and video monitoring are broad applications that aid in the understanding of human action in a video. The problem with action recognition algorithms such as 3D CNN, Two-stream network, and CNN-LSTM is that they have highly complex models including a lot of parameters resulting in difficulty while training them. Such models require high configuration machines for real-time human action recognition. Therefore, present research proposes the use of 2D skeleton features along with a KNN classifier based HAR system to overcome the aforementioned problems of complexity and response time.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | deep learning, human action recognition, KNN, machine learning, skeleton features |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Computing |
| ID Code: | 100671 |
| Deposited By: | Yanti Mohd Shah |
| Deposited On: | 30 Apr 2023 08:31 |
| Last Modified: | 30 Apr 2023 08:31 |
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