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- W1897079423 abstract "Leader identification is a crucial task in social analysis, crowd management and emergency planning. In this paper, we investigate a computational model for the individuation of leaders in crowded scenes. We deal with the lack of a formal definition of leadership by learning, in a supervised fashion, a metric space based exclusively on people spatiotemporal information. Based on Tarde's work on crowd psychology, individuals are modeled as nodes of a directed graph and leaders inherits their relevance thanks to other members references. We note this is analogous to the way websites are ranked by the PageRank algorithm. During experiments, we observed different feature weights depending on the specific type of crowd, highlighting the impossibility to provide a unique interpretation of leadership. To our knowledge, this is the first attempt to study leader identification as a metric learning problem." @default.
- W1897079423 created "2016-06-24" @default.
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- W1897079423 date "2015-06-01" @default.
- W1897079423 modified "2023-09-24" @default.
- W1897079423 title "Learning to identify leaders in crowd" @default.
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- W1897079423 doi "https://doi.org/10.1109/cvprw.2015.7301282" @default.
- W1897079423 hasPublicationYear "2015" @default.
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