Citation

Khatoon, Amna and Ullah, Asad and Ahmed, Ishfaq and Aslam, Ayesha and Roslee, Mardeni and Ahmad, Shabeer (2024) Enabling Smart Road Safety: Constrained Device Utilization for Accident Prevention in Traffic Congestion. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

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Abstract

Computer Vision (CV) analysis on constrained devices plays a crucial role in enhancing road safety and mitigating traffic congestion. This study demonstrates the integration of image segmentation and classification techniques into traffic monitoring systems on devices with limited computational power, such as dashboard cameras, smartphones, and IoT sensors. These technologies enable efficient real-time data collection and processing in densely populated urban areas, facilitating the immediate detection of irregular traffic patterns and potential collision points by leveraging edge computing and optimized algorithms. By implementing these analyses directly on constrained devices strategically placed within traffic systems, our approach provides timely responses to dynamic conditions, significantly reducing accident risks through early warnings and adaptive route suggestions. Our research also shows that CV systems can monitor driver behavior, detect attention or weariness, and give timely signals to improve driver response times. The data supports improving traffic signal timings, which might reduce vehicle idle times and improve traffic flow, thus increasing road safety and reducing environmental impact. The study provides a comprehensive methodology for implementing high-quality CV technologies in resource-constrained environments, highlighting advancements in edge computing and energy-efficient hardware. These advances underscore the importance of CV in building sustainable, resilient transport networks and its versatility in municipal and vehicle traffic management, enabling proactive measures to reduce accidents.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Computer Vision
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Feb 2025 02:32
Last Modified:	07 Feb 2025 02:32
URII:	http://shdl.mmu.edu.my/id/eprint/13391

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