Recently, social theories and empirical observations identified small groups and leaders as the basic elements which shape a crowd. This leads to an intermediate level of abstraction that is placed between the crowd as a flow of people, and the crowd as a collection of individuals. Consequently, automatic analysis of crowds in computer vision is also experiencing a shift in focus from individuals to groups and from small groups to their leaders. In this chapter, we present state-of-the-art solutions to the groups and leaders detection problem, which are able to account for physical factors as well as for sociological evidence observed over short time windows. The presented algorithms are framed as structured learning problems over the set of individual trajectories. However, the way trajectories are exploited to predict the structure of the crowd is not fixed but rather learned from recorded and annotated data, enabling the method to adapt these concepts to different scenarios, densities, cultures, and other unobservable complexities. Additionally, we investigate the relation between leaders and their groups and propose the first attempt to exploit leadership as prior knowledge for group detection.
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|Data di pubblicazione:||2017|
|Titolo:||From Groups to Leaders and Back|
|Autori:||Solera, Francesco; Calderara, Simone; Cucchiara, Rita|
|Digital Object Identifier (DOI):||10.1016/B978-0-12-809276-7.00010-2|
|Titolo del libro:||Group and Crowd Behavior for Computer Vision|
|Tutti i curatori:||Vittorio Murino, Marco Cristani, Shishir Shah and Silvio Savarese|
|Nome editore:||Academic Press|
|Appare nelle tipologie:||Capitolo/Saggio|
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