Trending Paths: A New Semantic-level Metric for Comparing Simulated and Real Crowd Data

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Wang, H ; Ondrej, J ; O'Sullivan, C (2017)
  • Publisher: Institute of Electrical and Electronics Engineers

We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.
  • References (47)
    47 references, page 1 of 5

    [11] K. H. Lee, M. G. Choi, Q. Hong, and J. Lee, “Group Behavior from Video: A Data-driven Approach to Crowd Simulation,” in SCA, 2007, pp. 109-118.

    [12] A. Lerner, E. Fitusi, Y. Chrysanthou, and D. Cohen-Or, “Fitting Behaviors to Pedestrian Simulations,” in SCA, 2009, pp. 199-208.

    [13] S. Kim, A. Bera, A. Best, R. Chabra, and D. Manocha, “Interactive and Adaptive Data-driven Crowd Simulation,” in 2016 IEEE VR, March 2016, pp. 29-38.

    [14] S. Kim, S. J. Guy, and D. Manocha, “Velocity-based Modeling of Physical Interactions in Multi-agent Simulations,” in SCA, 2013, pp. 125-133.

    [15] S. Lemercier, A. Jelic, R. Kulpa, J. Hua, J. Fehrenbach, P. Degond, C. Appert-Rolland, S. Donikian, and J. Pettre´, “Realistic Following Behaviors for Crowd Simulation,” Comp. Graph. Forum, vol. 31, no. 2, pp. 489-498, 2012.

    [16] R. McDonnell, M. Larkin, S. Dobbyn, S. Collins, and C. O'Sullivan, “Clone Attack! Perception of Crowd Variety,” ACM Trans. Graph., vol. 27, no. 3, pp. 26:1-26:8, 2008.

    [17] S. J. Guy, S. Kim, M. C. Lin, and D. Manocha, “Simulating Heterogeneous Crowd Behaviors Using Personality Trait Theory,” in SCA, 2011, pp. 43-52.

    [18] C. Ennis, C. Peters, and C. O'Sullivan, “Perceptual Effects of Scene Context and Viewpoint for Virtual Pedestrian Crowds,” ACM Trans. Appl. Percept., vol. 8, no. 2, pp. 10:1-10:22, 2011.

    [19] S. Kim, S. J. Guy, D. Manocha, and M. C. Lin, “Interactive Simulation of Dynamic Crowd Behaviors Using General Adaptation Syndrome Theory,” in I3D, 2012, pp. 55-62.

    [20] A. Golas, R. Narain, and M. Lin, “Hybrid Long-range Collision Avoidance for Crowd Simulation,” in I3D, 2013, pp. 29-36.

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