The topic of crowd modeling in computer vision assumes a single generic typology of crowd, which is very simplis- tic. In this paper we adopt a widely accepted taxonomy for crowds, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specific that they came to see" [6]. This can be found at the stadiums, amphitheaters, cinema, etc. In par- ticular, we propose a novel dataset, the Spectators Hockey (S-HOCK), which deals with hockey matches during an in- ternational tournament. The dataset considers 4 hockey matches, where hundreds of spectators are individually an- notated, capturing fine grained actions such as hands on hips, clapping hands, watching the cellphone etc., for a to- tal of more than 100 millions of annotations. Analyzing peo- ple at the stadium addresses different computer vision tasks, some of them are classic (crowd counting), while other are brand new (as the spectator categorization). For this reason, S-HOCK comes also with a set of protocols for dealing with all of them, and a set of baselines and novel approaches that define the best scores on all the tasks. Anyway, the perfor- mances are far from being errorless, and this witnesses the difficulty of the problem and that much can be done in the future.

The S-HOCK Dataset: Analyzing Crowds at the Stadium

Setti F;Bassetti C;
2015

Abstract

The topic of crowd modeling in computer vision assumes a single generic typology of crowd, which is very simplis- tic. In this paper we adopt a widely accepted taxonomy for crowds, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specific that they came to see" [6]. This can be found at the stadiums, amphitheaters, cinema, etc. In par- ticular, we propose a novel dataset, the Spectators Hockey (S-HOCK), which deals with hockey matches during an in- ternational tournament. The dataset considers 4 hockey matches, where hundreds of spectators are individually an- notated, capturing fine grained actions such as hands on hips, clapping hands, watching the cellphone etc., for a to- tal of more than 100 millions of annotations. Analyzing peo- ple at the stadium addresses different computer vision tasks, some of them are classic (crowd counting), while other are brand new (as the spectator categorization). For this reason, S-HOCK comes also with a set of protocols for dealing with all of them, and a set of baselines and novel approaches that define the best scores on all the tasks. Anyway, the perfor- mances are far from being errorless, and this witnesses the difficulty of the problem and that much can be done in the future.
2015
Istituto di Scienze e Tecnologie della Cognizione - ISTC
Inglese
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
International conference on Computer Vision and Pattern Recognition (CVPR 2015)
2039
2047
https://ieeexplore.ieee.org/document/7298815
IEEE-Institute Of Electrical And Electronics Engineers Inc.
Piscataway
STATI UNITI D'AMERICA
Sì, ma tipo non specificato
7-12 June 2015
Boston, MA, USA
spectator crowd
crowd analysis
spatio-temporal clustering
8
none
Conigliaro, D; Rota, P; Setti, F; Bassetti, C; Conci, N; Sebe, N; Cristani, ; M,
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/307226
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