Anonymization Pipelines in Traffic Video Analytics: A Comprehensive Study

Author(s)	Simran Sethi
Country	United States
Abstract	The more recent development of machine learning, artificial intelligence, and computer vision have allowed for the automated analysis of traffic videos for monitoring the road state, accident detection, and even driver behaviour analysis. These advancements, however, pose privacy challenges concerning the collection, retention, and processing of personally identifiable information-like faces and car number plates. This paper considers the design and implementation of anonymization pipelines for video analytics of traffic flow, paying special attention to the methods of removing or obscuring identifying features and preserving the usefulness of the data for other shallow tasks such as event detection, road-traffic, and driver behaviour classification. This work seeks to document practical applications and recommend policies for smart city and vehicular telematics deployments using existing literature, design of a new framework, and experimental data obtained in a deployment.
Field	Engineering
Published In	Volume 2, Issue 4, July-August 2020
Published On	2020-07-07
DOI	https://doi.org/10.36948/ijfmr.2020.v02i04.39042
Short DOI	https://doi.org/g88g6j

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International Journal For Multidisciplinary Research