Retail shops or restaurants are interested in real-time profiling analysis of customer visit patterns, which could enable efficient management and target marketing. They need to know not only how many people entered but also if they are visiting for the first time and keep track of their exact number. As a result, in this paper we define the new variant of unique counting for videos, that is counting new persons who have not already been counted in the past. To this end, we propose a complete real-time system which is able to perform detection, tracking and unique counting in the wild with user drawn gates. A fine-tuned network on persons body is used to extract descriptors which are more privacy-oriented. Experiments of the system on the challenging DukeMTMC dataset show that our method is able to effectively count people in real time and discern between the persons which do multiple passages through the gates.

Open Set Recognition for Unique Person Counting via Virtual Gates / Turchini, Francesco; Bruni, Matteo; Baecchi, Claudio; Uricchio, Tiberio; Del Bimbo, Alberto. - ELETTRONICO. - 11751:(2019), pp. 94-105. ((Intervento presentato al convegno ICIAP: International Conference on Image Analysis and Processing tenutosi a Trento nel September 9–13, 2019 [10.1007/978-3-030-30642-7_9].

Open Set Recognition for Unique Person Counting via Virtual Gates

Turchini, Francesco;Bruni, Matteo;Baecchi, Claudio
;
Uricchio, Tiberio;Del Bimbo, Alberto
2019

Abstract

Retail shops or restaurants are interested in real-time profiling analysis of customer visit patterns, which could enable efficient management and target marketing. They need to know not only how many people entered but also if they are visiting for the first time and keep track of their exact number. As a result, in this paper we define the new variant of unique counting for videos, that is counting new persons who have not already been counted in the past. To this end, we propose a complete real-time system which is able to perform detection, tracking and unique counting in the wild with user drawn gates. A fine-tuned network on persons body is used to extract descriptors which are more privacy-oriented. Experiments of the system on the challenging DukeMTMC dataset show that our method is able to effectively count people in real time and discern between the persons which do multiple passages through the gates.
Image Analysis and Processing -- ICIAP 2019 Proceedings, Part I
ICIAP: International Conference on Image Analysis and Processing
Trento
September 9–13, 2019
Turchini, Francesco; Bruni, Matteo; Baecchi, Claudio; Uricchio, Tiberio; Del Bimbo, Alberto
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE 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: https://hdl.handle.net/2158/1171646
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact