TR2017-085

Crowd Flow Completion From Partial Spatial Observations Using Kernel DMD


    •  Mansour, H., Benosman, M., Huroyan, V., "Crowd Flow Completion From Partial Spatial Observations Using Kernel DMD", International Conference on Sampling Theory and Applications (SampTA), July 2017.
      BibTeX TR2017-085 PDF
      • @inproceedings{Mansour2017jul,
      • author = {Mansour, Hassan and Benosman, Mouhacine and Huroyan, Vahan},
      • title = {Crowd Flow Completion From Partial Spatial Observations Using Kernel DMD},
      • booktitle = {International Conference on Sampling Theory and Applications (SampTA)},
      • year = 2017,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2017-085}
      • }
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  • Research Areas:

    Computational Sensing, Digital Video, Dynamical Systems

In this paper, we address the problem of estimating the total flow of a crowd of pedestrians from spatially limited observations. Our approach relies on identifying a dynamical system regime that characterizes the observed flow in a limited spatial domain by solving for the modes and eigenvalues of the corresponding Koopman operator. We develop a framework where we first approximate the Koopman operator by computing the kernel dynamic mode decomposition (DMD) operator for different flow regimes using fully observed training data. We then pose flow completion as a least squares problem constrained by the one step evolution of the kernel DMD operator. We present numerical experiments with simulated pedestrian flows and demonstrate that the proposed approach succeeds in completing the flow from limited spatial observations.