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A Real-Time Computer Vision Based Approach to Detection and Classification of Traffic Incidents by Mohammed Imran Basheer Ahmed; Rim Zaghdoud; Mohammed Salih Ahmed; Razan Sendi; Sarah Alsharif; Jomana Alabdulkarim; Bashayr Adnan Albin Saad; Reema Alsabt; Atta Rahman; Gomathi Krishnasamy is a Computer Science article available to read on EtoBox.

What is A Real-Time Computer Vision Based Approach to Detection and Classification of Traffic Incidents about?

To constructively ameliorate and enhance traffic safety measures in Saudi Arabia, a prolific number of AI (Artificial Intelligence) traffic surveillance technologies have emerged, including Saher, throughout the past years. However, rapidly detecting a vehicle incident can play a cardinal role in ameliorating the response speed of incident management, which in turn minimizes road injuries that have been induced by the accident’s occurrence. To attain a permeating effect in increasing the entailed demand for road traffic security and safety, this paper presents a real-time traffic incident detection and alert system that is based on a computer vision approach. The proposed framework consists of three models, each of which is integrated within a prototype interface to fully visualize the system’s overall architecture. To begin, the vehicle detection and tracking model utilized the YOLOv5 object detector with the DeepSORT tracker to detect and track the vehicles’ movements by allocating a unique identification number (ID) to each vehicle. This model attained a mean average precision (mAP) of 99.2%. Second, a traffic accident and severity classification model attained a mAP of 83.3% wh

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Author

Mohammed Imran Basheer Ahmed; Rim Zaghdoud; Mohammed Salih Ahmed; Razan Sendi; Sarah Alsharif; Jomana Alabdulkarim; Bashayr Adnan Albin Saad; Reema Alsabt; Atta Rahman; Gomathi Krishnasamy

Publisher

MDPI AG

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)