Open Access

ARTICLE

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

M. Zeeshan Sarwar¹, Mohammed Hamad Alatiyyah², Ahmad Jalal¹, Mohammad Shorfuzzaman³, Nawal Alsufyani³, Jeongmin Park^4,*

1 Department of Computer Science, Air University, Islamabad, Pakistan
2 Department of Computer Science, College of Sciences and Humanities in Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
4 Department of Computer Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do, 15073, Korea

* Corresponding Author: Jeongmin Park. Email:

Computers, Materials & Continua 2023, 75(1), 959-976. https://doi.org/10.32604/cmc.2023.035442

Received 21 August 2022; Accepted 04 November 2022; Issue published 06 February 2023

Abstract

In the last decade, there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras. They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios. Depth cues can improve the quality of object detection and recognition. The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets. The proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model (mWMM). mWMM is challenging to handle when the quality of the image is not good. Hence, the proposed RGB-D-based system uses depth cues for segmentation with the help of mWMM. Then it extracts multiple features from the segmented images. The selected features are fed to the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) for detecting objects. We achieved 92.13% of mean accuracy over NYUv1 dataset and 90.00% of mean accuracy for the Redweb_v1 dataset. Finally, their results are compared and the proposed model with CNN outperforms other state-of-the-art methods. The proposed architecture can be used in autonomous cars, traffic monitoring, and sports scenes.

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Keywords

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