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Robust Human Interaction Recognition Using Extended Kalman Filter

Tanvir Fatima Naik Bukht1, Abdulwahab Alazeb2, Naif Al Mudawi2, Bayan Alabdullah3, Khaled Alnowaiser4, Ahmad Jalal1, Hui Liu5,*

1 Faculty of Computing & Artificial Intelligence, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
5 Cognitive Systems Lab, University of Bremen, Bremen, 28359, Germany

* Corresponding Author: Hui Liu. Email: email

Computers, Materials & Continua 2024, 81(2), 2987-3002. https://doi.org/10.32604/cmc.2024.053547

Abstract

In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT for feature extraction. The next step is to perform feature discrimination using Gray Wolf. After that, the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive characteristics. The experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88% and 0.86% recognition rate.

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APA Style
Bukht, T.F.N., Alazeb, A., Mudawi, N.A., Alabdullah, B., Alnowaiser, K. et al. (2024). Robust human interaction recognition using extended kalman filter. Computers, Materials & Continua, 81(2), 2987-3002. https://doi.org/10.32604/cmc.2024.053547
Vancouver Style
Bukht TFN, Alazeb A, Mudawi NA, Alabdullah B, Alnowaiser K, Jalal A, et al. Robust human interaction recognition using extended kalman filter. Comput Mater Contin. 2024;81(2):2987-3002 https://doi.org/10.32604/cmc.2024.053547
IEEE Style
T.F.N. Bukht et al., “Robust Human Interaction Recognition Using Extended Kalman Filter,” Comput. Mater. Contin., vol. 81, no. 2, pp. 2987-3002, 2024. https://doi.org/10.32604/cmc.2024.053547



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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