We propose a new type of crowd analysis, focused on the spectator crowd, that is, people “interes... more We propose a new type of crowd analysis, focused on the spectator crowd, that is, people “interested in watching something spe- cific that they came to see” [1]. This scenario applies on stadiums, am- phitheaters etc., and shares some aspects with classical crowd moni- toring: actually, many people are simultaneously observed, so that per- person analysis is hard; however, here the dynamics of humans is more constrained, due to the architectural environment in which they are sit- uated; specifically, people are expected to stay in a fixed location most of the time, limiting their activities to applaud, support/heckle the play- ers or discuss with the neighbors. In this paper, we start facing this challenge by considering hockey matches, locating a videocamera 25-30 meters far from the bleachers, pointing at the crowd: in this scenario, ag- gregations of spectators that exhibit similar behavior are detected, and the behavior is classified into a set of predefined classes, highlighting the overall excitement. To these aims, in a first step we focus on individual frames, clustering local flow measures into spatial regions. The clustering is then extended by adding the temporal axis into the analysis, looking for non-randomic spatio-temporal clusters; to this aim, the Lempel-Ziv complexity is considered. This way, choral activities can emerge, indi- cating for example fan groups belonging to different teams. After this, with the adoption of entropic measures, the degree of excitement of such groups can be quantified.
We focus on the automated analysis of spectator crowd, that is, people watching sport contests al... more 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 [...] in- terested in watching something specific that they came to see” [2]. 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 ex- pected 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 so- cial 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.
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Keywords: spectator crowd, Lempel-Ziv complexity, spatio-temporal clustering