Cloud Based Scalable Video Processing Algorithm for Real-Time Automotive Safety Systems

Authors: Akshat Bhutiani

DOI: https://doi.org/10.5281/zenodo.14122783

Short DOI: https://doi.org/g8qttp

Country: USA

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Abstract: This paper presents a cloud based scalable video processing algorithm designed to enhance real-time automotive safety. The suggested algorithm makes use of cloud computing to process real-time video feeds from in-car cameras and sensors in order to quickly identify possible hazards like pedestrians, obstacles, and road conditions. The system ensures scalability through cloud resources and performs real-time object detection and classification using convolutional neural networks (CNNs), one of the most advanced deep learning models. The algorithm makes it possible to process high-resolution video streams more quickly and accurately by offloading computationally demanding tasks to the cloud. This improves response times and boosts overall vehicle safety. In addition to facilitating integration across a broad spectrum of automobiles, this scalable architecture makes it a practical means of augmenting the safety of driver-assisted and autonomous systems alike.

Keywords: real-time video processing, automotive safety systems, cloud computing, scalable algorithms

Paper Id: 231534

Published On: 2021-05-05

Published In: Volume 9, Issue 3, May-June 2021

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