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Smart city article details

Title	Deep Learning For Robust Vehicle Identification
ID_Doc	17884
Authors	Ramajo Ballester Á.; González Cepeda J.; Armingol Moreno J.M.
Year	2023
Published	Lecture Notes in Networks and Systems, 589 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-21065-5_29
Abstract	The level of precision of deep neural networks in visual perception tasks allows to capture crucial information from the environment for future projects, such as autonomous vehicles and smart cities. One possibility that this type of system would allow is the control and tracking of certain suspicious vehicles. Considering the use of this technology by police, it would facilitate the tracking of certain cars under investigation. With this vision, the objective of this work is the study of the current state-of-the-art of the methods and the development of a system that solves two tasks efficiently: the visual characterization and re-identification of vehicles and the license plates segmentation and character recognition. This dual identification can adapt to the environmental conditions, target distance and cameras capabilities and resolution. To test and validate this system, a custom dataset has been created to minimize the difference between lab and real environment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Deep learning; Smart cities; Vehicle re-identification

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