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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Mohd Norzali Haji Mohd

Universiti Tun Hussein Onn Malaysia

Author-ID : 2455886

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 8 Documents Claim Missing Document

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Title

Recognition of vehicle make and model in low light conditions Aymen Fadhil Abbas; Usman Ullah Sheikh; Mohd Norzali Haji Mohd
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents a method for vehicle make and model recognition (MMR) in low lighting conditions. While many MMR methods exist in the literature, these methods are designed to be used only in perfect operating conditions. The various camera configuration, lighting condition, and viewpoints cause variations in image quality. In the presented method, the vehicle is first detected, image enhancement is then carried out on the detected front view of the vehicle, followed by features extraction and classification. The performance is then examined on a low-light dataset. The results show around 6% improvement in the ability of MMR with the use of image enhancement over the same recognition model without image enhancement.

Food intake gesture monitoring system based-on depth sensor Muhammad Fuad bin Kassim; Mohd Norzali Haji Mohd
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Food intake gesture technology is one of a new strategy for obesity people managing their health care while saving their time and money. This approach involves combining face and hand joint point for monitoring food intake of a user using Kinect Xbox One camera sensor. Rather than counting calories, scientists at Brigham Young University found dieters who eager to reduce their number of daily bites by 20 to 30 percent lost around two kilograms a month, regardless of what they ate [1]. Research studies showed that most of the methods used to count bite are worn type devices which has high false alarm ratio. Today trend is going toward the non-wearable device. This sensor is used to capture skeletal data of user while eating and train the data to capture the motion and movement while eating. There are specific joint to be capture such as Jaw face point and wrist roll joint. Overall accuracy is around 94%. Basically, this increase in the overall recognition rate of this system.

Malaysian car plate localization using region-based convolutional neural network Tay Eng Liang; Usman Ullah Sheikh; Mohd Norzali Haji Mohd
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Automatic car plate localization and recognition system is a system that identifies the car plate location and recognizes the characters on the car plate input images. Within the automated system, the car plate localization stage is the first stage and is the most crucial stage as the success rate of the whole system depends heavily on it. In this paper, a Malaysian car plate localization system using Region-based Convolutional Neural Network (R-CNN) is proposed. Using transfer learning on the AlexNet CNN, the localization was greatly improved achieving best precision and recall rate of 95.19% and 97.84% respectively. Besides, the proposed R-CNN was able to localize car plates in complex scenarios such as under occlusion.

A non-invasive and non-wearable food intake monitoring system based on depth sensor Muhammad Fuad Kassim; Mohd Norzali Haji Mohd; Mohd Razali Md Tomari; Nor Surayahani Suriani; Wan Nurshazwani Wan Zakaria; Suhaila Sari
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2256

The food intake counting method showed a good significance that can lead to a successful weight loss by simply monitoring the food intake taken during eating. The device used in this project was Kinect Xbox One which used a depth camera to detect the motion of a person’s gesture and posture during food intake. Previous studies have shown that most of the methods used to count food intake device is worn device type. The recent trend is now going towards non-wearable devices due to the difficulty when wearing devices and it has high false alarm ratio. The proposed system gets data from the Kinect camera and monitors the gesture of the user while eating. Then, the gesture data is collected to be recognized and it will start counting the food intake taken by the user. The system recognizes the patterns of the food intake from the user by following the algorithm to analyze the gesture of the basic eating type and the system get an average accuracy of 96.2%. This system can help people who are trying to follow a proper way to avoid being overweight or having eating disorders by monitoring their meal intake and controlling their eating rate.

Co-Authors Aymen Fadhil Abbas Aymen Fadhil Abbas Fahad Taha AL-Dhief Mohd Razali Md Tomari Muhammad Fuad bin Kassim Muhammad Fuad Kassim Nor Surayahani Suriani Suhaila Sari Syadia Nabilah Mohd Safuan Tay Eng Liang Usman Ullah Sheikh Usman Ullah Sheikh Wan Nurshazwani Wan Zakaria

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