Open Access

ARTICLE

Human Pose Estimation and Object Interaction for Sports Behaviour

by Ayesha Arif¹, Yazeed Yasin Ghadi², Mohammed Alarfaj³, Ahmad Jalal¹, Shaharyar Kamal¹, Dong-Seong Kim^4,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science and Software Engineering, Al Ain University, Al Ain, 15551, UAE
3 Department of Electrical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
4 Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea

* Corresponding Author: Dong-Seong Kim. Email:

Computers, Materials & Continua 2022, 72(1), 1-18. https://doi.org/10.32604/cmc.2022.023553

Received 12 September 2021; Accepted 16 November 2021; Issue published 24 February 2022

Abstract

In the new era of technology, daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds. To understand the scenes and activities from human life logs, human-object interaction (HOI) is important in terms of visual relationship detection and human pose estimation. Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained. Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures, occluded regions, and unsatisfactory detection of objects, especially small-sized objects. The existing HOI detection techniques are instance-centric (object-based) where interaction is predicted between all the pairs. Such estimation depends on appearance features and spatial information. Therefore, we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI. Furthermore, we detect the human body parts by using the Gaussian Matric Model (GMM) followed by object detection using YOLO. We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm. The interactions are linked with the human and object to predict the actions. The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.

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Keywords

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