Open Access

ARTICLE

Exploiting Human Pose and Scene Information for Interaction Detection

Manahil Waheed¹, Samia Allaoua Chelloug^2,*, Mohammad Shorfuzzaman³, Abdulmajeed Alsufyani³, Ahmad Jalal¹, Khaled Alnowaiser⁴, Jeongmin Park⁵

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
4 Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
5 Department of Computer Engineering, Korea Polytechnic University, Siheung-si, Gyeonggi-do, 237, Korea

* Corresponding Author: Samia Allaoua Chelloug. Email:

Computers, Materials & Continua 2023, 74(3), 5853-5870. https://doi.org/10.32604/cmc.2023.033769

Received 27 June 2022; Accepted 22 September 2022; Issue published 28 December 2022

Abstract

Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning. This wide range of applications has attracted many researchers to this field. Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information. There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene. The main objectives of this research include critically examining the importance of all these elements in determining the interaction, estimating human pose through image foresting transform (IFT), and detecting the performed interactions based on an optimized multi-feature vector. The proposed methodology has six main phases. The first phase involves preprocessing the images. During preprocessing stages, the videos are converted into image frames. Then their contrast is adjusted, and noise is removed. In the second phase, the human-object pair is detected and extracted from each image frame. The third phase involves the identification of key body parts of the detected humans using IFT. The fourth phase relates to three different kinds of feature extraction techniques. Then these features are combined and optimized during the fifth phase. The optimized vector is used to classify the interactions in the last phase. The MSR Daily Activity 3D dataset has been used to test this model and to prove its efficiency. The proposed system obtains an average accuracy of 91.7% on this dataset.

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Keywords

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