Open Access

ARTICLE

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Tanvir Fatima Naik Bukht¹, Naif Al Mudawi², Saud S. Alotaibi³, Abdulwahab Alazeb², Mohammed Alonazi⁴, Aisha Ahmed AlArfaj⁵, Ahmad Jalal¹, Jaekwang Kim^6,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia
3 Information Systems Department, Umm Al-Qura University, Makkah, Saudi Arabia
4 Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
6 Convergence Program for Social Innovation, Sungkyunkwan University, Suwon, 03063, Korea

* Corresponding Author: Jaekwang Kim. Email:

(This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)

Computers, Materials & Continua 2023, 77(2), 1557-1573. https://doi.org/10.32604/cmc.2023.041335

Received 18 April 2023; Accepted 11 August 2023; Issue published 29 November 2023

Abstract

Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems’ ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to enhance colors in video frames, making them more vibrant and visually appealing, especially in low-contrast or washed-out scenes. Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method. Feature extraction uses the features from Accelerated Segment Test (FAST), Oriented FAST, and Rotated BRIEF (ORB) techniques. The application of Quadratic Discriminant Analysis (QDA) for feature fusion and discrimination enables high-dimensional data to be effectively analyzed, thus further enhancing the classification process. It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities. The impressive accuracy rates of 93% and 94.6% achieved in the BIT-Interaction and UT-Interaction datasets respectively, highlight the success and reliability of the proposed technique. The proposed approach addresses challenges in various domains by focusing on frame improvement, silhouette and feature extraction, feature fusion, and HMM classification. This enhances data quality, accuracy, adaptability, reliability, and reduction of errors.

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Keywords

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