ATTENTO: ATTENTion Observed for Automated Spectator Crowd Analysis

Abstract

Abstract. We propose a new type of crowd analysis, focused on the spectator crowd, that is, people "interested in watching something specific that they came to see" [1]. This scenario applies on stadiums, amphitheaters etc., and shares some aspects with classical crowd monitoring: actually, many people are simultaneously observed, so that perperson analysis is hard; however, here the dynamics of humans is more constrained, due to the architectural environment in which they are situated; specifically, 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 considering hockey matches, locating a videocamera 25-30 meters far from the bleachers, pointing at the crowd: in this scenario, aggregations 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, indicating 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.

Statistical Pattern Recognition Meets Formal Ontologies

“…The first methodological results obtained by following this new direction on object recognition can be found in [12,13,2] and on video understanding in [4,3]. Together with papers, practical industrial applications tested as prototypes during the Winter Universiade in Trento 1 are going to be finalized, showing that the potentialities of this new perspective [11] are not limited to a mere theoretical dimension [5].…”

“…Such symbolic stances have been long studied in the sociological literature and can be deployed to give much more fine-grained scene automatic interpretations. Our first studies have been focused on getting video data, and to study ontologies to describe the activities going on in a stadium [4,3,5]. An ontology, if appropriately instructed by the outcomes of sociological studies, should be able to infer meaningful symbolic behavior of individuals or groups in the crowd, given the composition of elementary movements performed by spectators, like sitting with crossed legs, standing up, clapping hands etc.…”

Sleepers' lag - study on motion and attention

“…With scope well beyond the classroom, attempts have already been made in interpreting the behaviours of large groups at sporting events (Conigliaro, Setti, Bassetti, Ferrario, & Cristani, 2013) and in public spaces in general (Bazzani et al, 2013). The first results showed promise, but with the crudeness of the initial findings, we are again reminded of the complexity of human interaction.…”

Holistic Analysis of the Classroom

“…Other effects in group behaviour such as automatic crowd segmentation based on motion have been observed in spectator sports [8]. With the introduction of reliable pose estimations, explorations spread into body movement, with the cost of introducing a depth-camera capturing constraints [28].…”