This paper aims to develop a method that can accurately count vehicles from images of parking areas captured by smart cameras. To this end, we have proposed a deep learning-based approach for car detection that permits the input images to be of arbitrary perspectives, illumination, and occlusions. No other information about the scenes is needed, such as the position of the parking lots or the perspective maps. This solution is tested using Counting CNRPark-EXT, a new dataset created for this specific task and that is another contribution to our research. Our experiments show that our solution outperforms the state-of-the-art approaches.

Counting Vehicles with Cameras

Luca Ciampi;Giuseppe Amato;Fabrizio Falchi;Claudio Gennaro;
2018

Abstract

This paper aims to develop a method that can accurately count vehicles from images of parking areas captured by smart cameras. To this end, we have proposed a deep learning-based approach for car detection that permits the input images to be of arbitrary perspectives, illumination, and occlusions. No other information about the scenes is needed, such as the position of the parking lots or the perspective maps. This solution is tested using Counting CNRPark-EXT, a new dataset created for this specific task and that is another contribution to our research. Our experiments show that our solution outperforms the state-of-the-art approaches.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/1142549
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact