MOTION TARGET MONITORING AND RECOGNITION IN VIDEO SURVEILLANCE USING CLOUD–EDGE–IOT AND MACHINE LEARNING TECHNIQUES

Amal K. Alkhalifa, Belal Zaqaibeh, Siwar Ben Haj Hassine, Abdulsamad Ebrahim Yahya, Wafa Sulaiman Almukadi, Shaymaa Sorour, Yazeed Alzahrani and Jihen Majdoubi
Additional contact information
Amal K. Alkhalifa: Department of Computer Science and Information Technology, Applied College Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Belal Zaqaibeh: ��Faculty of Science and Information Technology, Jadara University, Irbid, Jordan
Siwar Ben Haj Hassine: ��Department of Computer Science, College of Science & Art at Mahayil King Khalid University, Abha, Asir, Saudi Arabia
Abdulsamad Ebrahim Yahya: �Department of Information Technology, College of Computing and Information Technology Northern Border, University, Arar, Saudi Arabia
Wafa Sulaiman Almukadi: �Department of Software Engineering, College of Engineering and Computer Science University of Jeddah, Jeddah, Saudi Arabia
Shaymaa Sorour: ��Department of Management Information Systems, College of Business Administration King Faisal University, Al-Ahsa 31982, Saudi Arabia
Yazeed Alzahrani: *Department of Computer Engineering, College of Engineering in Wadi Addawasir Prince Sattam, Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Jihen Majdoubi: ��†Department of Computer Science, College of Science and Humanities at Alghat, Majmaah University Al-Majmaah 11952, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-27

Abstract: We are aware that autonomous vehicle handles camera and LiDAR data pipelines and uses the sensor pictures to provide an autonomous object identification solution. While current research yields reasonable results, it falls short of offering practical solutions. For example, lane markings and traffic signs may become obscured by accumulation on roads, making it unsafe for a self-driving car to navigate. Moreover, the car’s sensors may be severely hindered by intense rain, snow, fog, or dust storms, which could endanger human safety. So, this research introduced Multi-Sensor Fusion and Segmentation for Deep Q-Network (DQN)-based Multi-Object Tracking in Autonomous Vehicles. Improved Adaptive Extended Kalman Filter (IAEKF) for noise reduction, Normalized Gamma Transformation-based CLAHE (NGT-CLAHE) for contrast enhancement, and Improved Adaptive Weighted Mean Filter (IAWMF) for adaptive thresholding have been used. A novel multi-segmentation using several segmentation methods and degrees dependent on the orientation of images has been used. DenseNet (D Net)-based multi-image fusion provides faster processing speeds and increased efficiency. The grid map-based pathways and lanes are chosen using the Energy Valley Optimizer (EVO) technique. This method easily achieves flexibility, robustness, and scalability by simplifying the complex activities. Furthermore, the YOLOv7 model is used for classification and detection. Metrics like velocity, accuracy rate, success rate, success ratio, and mean-squared error are used to assess the proposed method.

Keywords: Multi-Sensor Fusion; Segmentation; DenseNet (D Net); Self-Driving Vehicles; YOLOv7 Model; Energy Valley Optimizer (EVO) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400134

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