Author: Daniele Di Mauro

Affiliation: Department of Mathematics and Informatics, University of Catania, Catania and Italy

Keyword(s): Parking, Deep Learning, Semantic Segmentation, Object Detection, Image Classification, Scene Adaptation, Counting.

Abstract: Smart cities is one of the new frontier of the Computer Vision community. The major part of world-wide population moved to urban areas, after such process many issues of major cities have worsened, e.g. air pollution, traffic, security. The increase of security cameras and the improvements of Computer Vision algorithm can be a good solution for many of those problems. Park Smart s.r.l., a company located in Catania, believes that Computer Vision can be the answer for parking space management. Their aim is to help private companies and public administrations to manage free entry parking areas, as well closed ones, in order to offer better services to the final customer i.e. the drivers and to increase the revenue per stall for public administrations. The architecture of the system follow the Edge Computing design which brings the Computer Vision computation close to the parking area. The main problem the company has to face is to find a fast way to deploy working solutions, lowering the labeling effort to the minimum, across different scene, cities, parking areas. During the three years of doctoral studies we have tried to solve the problem through the use of various methods such as Semi-Supervised Learning, Counting and Scene Adaptation through Image Classification, Object Detection and Semantic Segmentation. (More)

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Paper citation in several formats:
Di Mauro, D. (2018). Scene Understanding for Parking Space Management.In Doctoral Consortium - DCETE, ISBN , pages 3-11

author={Daniele Di Mauro.},
title={Scene Understanding for Parking Space Management},
booktitle={Doctoral Consortium - DCETE,},


JO - Doctoral Consortium - DCETE,
TI - Scene Understanding for Parking Space Management
SN -
AU - Di Mauro, D.
PY - 2018
SP - 3
EP - 11
DO -

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