Viewing the Viewers: A Novel Challenge for Automated Crowd Analysis
We focus on the automated analysis of spectator crowd, that is, people watching sport contests alive (in stadiums, amphitheaters etc.), or, more generally, people “watching the activities of an event […] interested in watching something specific that they came to see” . This scenario differs substantially from the typical crowd analysis setting (e.g. pedestrians): here the dynamics of humans is more constrained, due to the architectural environments in which they are situated; people are expected to stay in a fixed location most of the time, limiting their activities to applaud, support/heckle the players or discuss with the neighbors. In this paper, we start facing this challenge by following a social signal processing approach, which grounds computer vision techniques in social theories. More specifically, leveraging on social theories describing expressive bodily conduct, we will show how, by using computer vision techniques, it is possible to distinguish fan groups belonging to different teams by automatically detecting their liveliness in different moments of the match, even when they are merged in the stands. Moreover, we will show how, only by automatically detecting crowd’s motions on the stands, it is possible to single out the most salient events of the match, like goals, fouls or shots on goal.
Keywordsspectator crowd crowd analysis spatio-temporal clustering
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- 1.Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detection. In: ICPR, pp. 175–178 (2006), http://dx.doi.org/10.1109/ICPR.2006.806
- 3.Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: ICCV, pp. 545–551 (2009)Google Scholar
- 4.Cristani, M., Murino, V., Vinciarelli, A.: Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space. In: CVPRW, pp. 51–58 (2010)Google Scholar
- 5.Cristani, M., Pesarin, A., Vinciarelli, A., Crocco, M., Murino, V.: Look at who’s talking: Voice activity detection by automated gesture analysis. In: AML Workshops, pp. 72–80 (2011)Google Scholar
- 7.Goffman, E.: Behaviour in Public Places. Free Press of Glencloe. Notes on the Social Organization of Gatherings (1963)Google Scholar
- 10.Heath, C., Hindmarsh, J., Luff, P.: Video in Qualitative Research. Analysing Social Interaction in Everyday Life. Sage, London (2010)Google Scholar
- 12.Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: CVPR, pp. 1446–1453 (2009)Google Scholar
- 13.Kratz, L., Nishino, K.: Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: CVPR, pp. 693–700 (2010)Google Scholar
- 15.Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: CVPR, pp. 1975–1981 (2010)Google Scholar
- 16.Raghavendra, R., Del Bue, A., Cristani, M., Murino, V.: Abnormal crowd behavior detection by social force optimization. In: HBU, pp. 134–145 (2011), http://dx.doi.org/10.1007/978-3-642-25446-8_15
- 20.Zhong, H., Shi, J., Visontai, M.: Detecting unusual activity in video. In: CVPR, pp. 819–826 (2004)Google Scholar