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Papers by majdi sukkar

Research paper thumbnail of A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems

IEEE Access, 2024

This paper surveys real-time object detection literature critically and analytically, focusing pa... more This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems.

Research paper thumbnail of Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

MDPI Information, 2024

Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pe... more Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning.Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.

Research paper thumbnail of Improve Detection and Tracking of Pedestrian Subclasses by Pre-Trained Models

Journal of Advanced Engineering and Computation, 2022

There are sub-classes of pedestrians that can be defined and it is important to distinguish betwe... more There are sub-classes of pedestrians that can be defined and it is important to distinguish between them for the detection in autonomous vehicle applications, such as elderly, and children, to reduce the risk of collision. It is necessary to talk about effective pedestrian tracking besides detection so that object remains accurately monitored, here the effective pre-trained algorithms come to achieve this goal in real-time. In this paper, we make a comparison between the detection and tracking algorithms, we applied the transfer learning technique to train the detection model on new sub-classes, after making Images augmentation in previous work [1], we got better results in detection, reached 0.81 mAP in real-time by using Yolov5 model, with a good tracking performance by the tracking algorithm dependent on detection Deep-SORT.

Research paper thumbnail of Real-Time Pedestrians Detection by YOLOv5

2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021

Real-time detection of objects is receiving growing attention. The pedestrian is the most critica... more Real-time detection of objects is receiving growing attention. The pedestrian is the most critical object that needs to be detecting and tracking by autonomous vehicles. The major challenges to this mission are caused by the difference in objects like pedestrians in age, gender, clothing, lighting, backgrounds, and occlusion. This paper starts with a brief introduction of problem-related to pedestrians, objects detection framework and Neural Networks Algorithms, and Real-Time Systems. And we focus on pedestrians as moving objects that need to detect, track, and solve problems related to computer vision. And based on our study we present a suggested solution for solving problems related to Pedestrians Detection in real-time particular. These techniques aim to be used in many applications such as Autonomous Vehicles, and Advanced Driver Assistance Systems (ADAS).

Thesis Chapters by majdi sukkar

Research paper thumbnail of Face Recognition Based on Attendance System Phase I

Majdi Sukkar, 2022

In present academic system, regular class attendance of students plays a significant role in perf... more In present academic system, regular class attendance of students plays a significant role in performance assessment and quality monitoring. The conventional methods practiced in most of the educational institutions are by calling names, which is highly time-consuming.
The human face is one of the natural traits that can uniquely identify an individual. Therefore, it is used to trace identity as the possibilities for multi-faces to deviate or being duplicated is low. In this project, face databases will be created to pump data into the recognizer algorithm. Then, during the attendance taking session, faces will be compared against the database to seek for identity. The proses start at specific time, and when an individual is identified, its attendance will be taken down automatically saving necessary information into a database system. At the end of the day .
In the proposed system, a Convolutional Neural Network (CNN) is used to detect faces in images, and deep learning is used in the process. Thus, the computer can recognize faces automatically.
The main purpose of this project is to build a face recognition-based attendance monitoring system for Syrian Private University to enhance and upgrade the current attendance process into more efficient and effective as compared to before.

Research paper thumbnail of A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems

IEEE Access, 2024

This paper surveys real-time object detection literature critically and analytically, focusing pa... more This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems.

Research paper thumbnail of Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

MDPI Information, 2024

Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pe... more Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning.Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.

Research paper thumbnail of Improve Detection and Tracking of Pedestrian Subclasses by Pre-Trained Models

Journal of Advanced Engineering and Computation, 2022

There are sub-classes of pedestrians that can be defined and it is important to distinguish betwe... more There are sub-classes of pedestrians that can be defined and it is important to distinguish between them for the detection in autonomous vehicle applications, such as elderly, and children, to reduce the risk of collision. It is necessary to talk about effective pedestrian tracking besides detection so that object remains accurately monitored, here the effective pre-trained algorithms come to achieve this goal in real-time. In this paper, we make a comparison between the detection and tracking algorithms, we applied the transfer learning technique to train the detection model on new sub-classes, after making Images augmentation in previous work [1], we got better results in detection, reached 0.81 mAP in real-time by using Yolov5 model, with a good tracking performance by the tracking algorithm dependent on detection Deep-SORT.

Research paper thumbnail of Real-Time Pedestrians Detection by YOLOv5

2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021

Real-time detection of objects is receiving growing attention. The pedestrian is the most critica... more Real-time detection of objects is receiving growing attention. The pedestrian is the most critical object that needs to be detecting and tracking by autonomous vehicles. The major challenges to this mission are caused by the difference in objects like pedestrians in age, gender, clothing, lighting, backgrounds, and occlusion. This paper starts with a brief introduction of problem-related to pedestrians, objects detection framework and Neural Networks Algorithms, and Real-Time Systems. And we focus on pedestrians as moving objects that need to detect, track, and solve problems related to computer vision. And based on our study we present a suggested solution for solving problems related to Pedestrians Detection in real-time particular. These techniques aim to be used in many applications such as Autonomous Vehicles, and Advanced Driver Assistance Systems (ADAS).

Research paper thumbnail of Face Recognition Based on Attendance System Phase I

Majdi Sukkar, 2022

In present academic system, regular class attendance of students plays a significant role in perf... more In present academic system, regular class attendance of students plays a significant role in performance assessment and quality monitoring. The conventional methods practiced in most of the educational institutions are by calling names, which is highly time-consuming.
The human face is one of the natural traits that can uniquely identify an individual. Therefore, it is used to trace identity as the possibilities for multi-faces to deviate or being duplicated is low. In this project, face databases will be created to pump data into the recognizer algorithm. Then, during the attendance taking session, faces will be compared against the database to seek for identity. The proses start at specific time, and when an individual is identified, its attendance will be taken down automatically saving necessary information into a database system. At the end of the day .
In the proposed system, a Convolutional Neural Network (CNN) is used to detect faces in images, and deep learning is used in the process. Thus, the computer can recognize faces automatically.
The main purpose of this project is to build a face recognition-based attendance monitoring system for Syrian Private University to enhance and upgrade the current attendance process into more efficient and effective as compared to before.