The world-wide growth of population in urban areas demands for the development of sustainable technologies to manage city services, such as transportation, in an efficient way. Motivated by the cost-effectiveness of image-based solutions, in this paper we investigate the exploitation of techniques based on image classification and object detection to count cars and non-empty stalls in parking areas. The analysis is performed on a dataset of images collected in a real parking area. Results show that techniques based on image classification are very effective when parking stalls are delimited by marking lines and the geometry of the scene is known in advance.

A comparison of techniques based on image classification and object detection to count cars and non-empty stalls in parking spaces

Di Mauro D.;Furnari A.;Battiato S.;Farinella G. M.
2018

Abstract

The world-wide growth of population in urban areas demands for the development of sustainable technologies to manage city services, such as transportation, in an efficient way. Motivated by the cost-effectiveness of image-based solutions, in this paper we investigate the exploitation of techniques based on image classification and object detection to count cars and non-empty stalls in parking areas. The analysis is performed on a dataset of images collected in a real parking area. Results show that techniques based on image classification are very effective when parking stalls are delimited by marking lines and the geometry of the scene is known in advance.
Classification; Counting; Deep Learning; Object Detection; Smart Cities
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/372952
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