Open Access

ARTICLE

Robust Human Interaction Recognition Using Extended Kalman Filter

Tanvir Fatima Naik Bukht¹, Abdulwahab Alazeb², Naif Al Mudawi², Bayan Alabdullah³, Khaled Alnowaiser⁴, Ahmad Jalal¹, Hui Liu^5,*

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:

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

Received 03 May 2024; Accepted 04 August 2024; Issue published 18 November 2024

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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Keywords

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