2015
DOI: 10.1109/jstsp.2014.2365765
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Abstract: There have been many studies in the literature on social group recognition of crowds of pedestrians. However, most of these studies have approached the problem from a static point of view. A study on the dynamic property of social groups among people over time can provide significant insight into human behaviors and events. Inspired by sociological models of human collective behavior, in this work, we present a framework for characterizing hierarchical social groups based on evolving tracklet interaction netwo… Show more

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Cited by 24 publications
(11 citation statements)
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“…Surveillance, entertainment and social sciences are examples of fields that can benefit from the development of this area of study. Literature presents different applications of crowd analysis, like counting people in crowds [9,6], group and crowd movement and formation [41,45,38,26] and detection of social groups in crowds [40,39,21,10]. Normally, these approaches are based on personal tracking or optical flow algorithms, and handle with features like walking speed, directions and distances over time.…”
Section: Introductionmentioning
confidence: 99%
“…Surveillance, entertainment and social sciences are examples of fields that can benefit from the development of this area of study. Literature presents different applications of crowd analysis, like counting people in crowds [9,6], group and crowd movement and formation [41,45,38,26] and detection of social groups in crowds [40,39,21,10]. Normally, these approaches are based on personal tracking or optical flow algorithms, and handle with features like walking speed, directions and distances over time.…”
Section: Introductionmentioning
confidence: 99%
“…We evaluate our approach on four data sets: the CAVIAR data set [8], the TownCentre data set [35], the PETS2009 data set [36], and the UNIV data set [37]. The popular evaluation metrics defined in [38] and the CLEAR MOT metrics defined in [39] are used for performance comparison.…”
Section: Methodsmentioning
confidence: 99%
“…Grouping pedestrians has been presented in [8] depending on evolving track interaction network (ETIN). Tracks of pedestrians are represented as nodes whereas their behavior as weighted edges.…”
Section: Grouping Social Objects In Snsmentioning
confidence: 99%