Education
MSc. Electrical and Computer Engineering , Abu Dhabi University, Abu Dhabi-UAE
BSc. Computer Engineering, Al-Balqa Applied University, Amman-Jordan
Research Interests
- AI in Robotics and Object Tracking
- Sensor Design and Simulation
- Solar Energy Forecasting using AI
- Data Analysis in Photovoltaic Systems
- AI for Renewable Energy Optimization
Selected Publications
Applied Sciences (Switzerland), 2022, 12(19), 9408
Proceedings - 2022 23rd International Arab Conference on Information Technology, ACIT 2022, 2022
Teaching Courses
- Physics 1 lab
- Physics 2 lab
- Digital Logic Design Lab
- Circuits Lab
- Electronics LAb
- Networks Lab
- Communication Lab
- Microprocessor and assembly language lab
- Calculus Intensive
- Physics Intensive
- Computer Skills
Memberships
Active member in the Jordanian Engineers Association (JEA) since April 2009
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all.
This person’s work contributes towards the following SDG(s):
Multiple Object Tracking in Robotic Applications: Trends and Challenges
Published in: Applied Sciences Journal
Sep 20, 2022
The recent advancement in autonomous robotics is directed toward designing a reliable system that can detect and track multiple objects in the surrounding environment for navigation and guidance purposes. This paper aims to survey the recent development in this area and present the latest trends that tackle the challenges of multiple object tracking, such as heavy occlusion, dynamic background, and illumination changes. Our research includes Multiple Object Tracking (MOT) methods incorporating the multiple inputs that can be perceived from sensors such as cameras and Light Detection and Ranging (LIDAR). In addition, a summary of the tracking techniques, such as data association and occlusion handling, is detailed to define the general framework that the literature employs. We also provide an overview of the metrics and the most common benchmark datasets, including Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), MOTChallenges, and University at Albany DEtection and TRACking (UA-DETRAC), that are used to train and evaluate the performance of MOT. At the end of this paper, we discuss the results gathered from the articles that introduced the methods. Based on our analysis, deep learning has introduced significant value to the MOT techniques in recent research, resulting in high accuracy while maintaining real-time processing.