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Integrating Object Detection and Optical Flow Analysis for Real-time Road Accident Detection | IEEE Conference Publication | IEEE Xplore

Integrating Object Detection and Optical Flow Analysis for Real-time Road Accident Detection


Abstract:

This paper proposes an innovative approach for enhancing road safety using improved traffic surveillance, which uses deep learning and computer vision techniques to detec...Show More

Abstract:

This paper proposes an innovative approach for enhancing road safety using improved traffic surveillance, which uses deep learning and computer vision techniques to detect car accidents. By combining the Lucas-Kanade approach and the YOLOv4 model, the system demonstrates practical application in real-world traffic monitoring, as well as effectiveness under a variety of testing scenarios. Despite issues with camera angles and quality, the research opens the door to future improvements, such as the use of more advanced algorithms to overcome present limitations and expanding the variety of traffic scenarios that automated surveillance systems may address. This contributes to the smart transportation systems domain by providing a new perspective on traffic safety management through technology innovation.
Date of Conference: 14-16 June 2024
Date Added to IEEE Xplore: 30 August 2024
ISBN Information:
Conference Location: Changsha, China

I. Introduction

In the current era, the exponential growth in vehicle numbers has underscored the critical importance of traffic surveillance systems in our daily lives, serving not just to enhance road safety but also to alleviate traffic congestion and refine the efficiency of transportation networks. These systems, leveraging an array of technologies such as CCTV cameras, radar detectors, and various sensors, meticulously collect and analyze data about traffic patterns, speeds, and vehicle types. Such comprehensive data analysis is pivotal for identifying areas plagued by congestion, incidents of accidents, and other disruptions that may impede traffic flow [5].

References

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