Hybrid Deep Learning Approach for Efficient Outdoor Parking Monitoring in Smart Cities
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Universidad de Deusto
info
- Hugo Sanjurjo González (coord.)
- Iker Pastor López (coord.)
- Pablo García Bringas (coord.)
- Héctor Quintián (coord.)
- Emilio Corchado (coord.)
Editorial: Springer International Publishing AG
ISBN: 978-3-030-86271-8, 978-3-030-86270-1
Año de publicación: 2021
Páginas: 463-474
Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)
Tipo: Aportación congreso
Resumen
The problem of transport is one of the fundamental problems in today’s large cities and one of the fundamental pillars on which work is being done under the paradigm of smart cities. Although the use of public transport is encouraged in cities, there are always problems related to private transport parking availability. This work focuses on the development of an algorithm for monitoring parking spaces in open-air car parks, which is effective and simple from the point of view of the costs required for its deployment and robust in detecting the state of the car park. To this end, an algorithm based on Deep Learning has been developed for processing the status images of different car parks, obtaining real-time information on the real status of the car park, without the need for extensive deployment of devices in the car park. The proposed solution has been shown to outperform other image-based solutions for the same problem.