Motion is a strong clue for unsupervised grouping of individuals in a crowded environment. We show that collective motion in the crowd can be discovered by temporal analysis of points trajectories. First k-NN graph is constructed to represent the topological structure of point trajectories detected in crowd. Then the data-driven graph segmentation and clustering helps to reveal the interaction of individuals even when mixed motion is presented in data. The method was evaluated against the latest state-of-the-art methods and achieved better performance by more than 20%.
CITATION STYLE
Trojanová, J., Křehnáč, K., & Brémond, F. (2016). Data-driven motion pattern segmentation in a crowded environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9914 LNCS, pp. 760–774). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_53
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