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Data-Driven Motion Pattern Segmentation in a Crowded Environments

Abstract : 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 seg-mentation 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 percent.
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Contributor : Soumik Mallick <>
Submitted on : Wednesday, July 25, 2018 - 6:53:13 PM
Last modification on : Wednesday, October 10, 2018 - 10:10:10 AM
Long-term archiving on: : Friday, October 26, 2018 - 3:59:25 PM


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  • HAL Id : hal-01849288, version 1



Jana Trojanova, Karel Křehnač, François Bremond. Data-Driven Motion Pattern Segmentation in a Crowded Environments. ECCV workshop on Crowd, Oct 2016, Amsterdam,, Netherlands. ⟨hal-01849288⟩



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