Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis

Capturing and understanding crowd dynamics is an important problem under
diverse perspectives. From sociology to safety management, modeling and pre-
dicting the crowd presence and its dynamics, possibly preventing dangerous
activities, is absolutely crucial. In the literature, crowd has been classied un-
der dierent categories depending on size and focus of attention. This chapter
focuses on spectator crowd, namely that formed by people whose behavior is
constrained by a structured environment, whose focus of attention is mainly
shared, directed to a specic event. We rst propose the backbone of an on-
tology of spectator crowd behavior based on a foundational analysis of both
related literature and S-Hock, a massive annotated video dataset on crowd be-
havior during hockey events. Then, we present a new methodological approach
integrating ontological reasoning, performed with a new description logics based
temporal formalism, with computer vision algorithms, allowing for automatic
recognition of events happening in the playground, based on the behavior of the
crowd in the stands.

Publication type: 
Contributo in volume
Author or Creator: 
Davide Conigliaro
Roberta Ferrario
Céline Hudelot
Daniele Porello
Elsevier ScienceDirec, Amsterdam, NLD
Integrating Computer Vision Algorithms and Ontologies for Spectator Crowd Behavior Analysis, edited by Murino, V., Cristani, M., Shah S., Savarese, S., pp. 297–319. Amsterdam: Elsevier ScienceDirec, 2017
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