Abstract
Road safety requires an understanding of traffic rules. It is also not just the responsibility of oneself but the coordination of every individual on the road to be aware and alert to avoid accidents. The objective of the paper is to analyze the impact of the accident and identify the vehicle which is being prone to accidents using image classification through machine learning. Machine learning provides the system with an ability to automatically learn and improve from the given dataset without human intervention or assistance. It looks for patterns in the data and takes a decision accordingly. The training process involves the following steps: collecting the images, annotating the image, data ingestion, and data processing. This paper follows the convolutional neural network algorithm that takes image inputs, assigns various aspects to images, and differentiates them from one another. The image recognition model automatically determines whether the incident in the given image is an accident with the help of bounding boxes. These bounding boxes surround themselves on vehicles which are prone to accidents.
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Vishnu, A., Sushmitha, S., Jacob, T.S., David Maxim Gururaj, A., Dhanasekar, S. (2023). Automated Road Surveillance System Using Machine Learning. In: Venkataraman, N., Wang, L., Fernando, X., Zobaa, A.F. (eds) Big Data and Cloud Computing. ICBCC 2022. Lecture Notes in Electrical Engineering, vol 1021. Springer, Singapore. https://doi.org/10.1007/978-981-99-1051-9_5
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