LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wang, H; Ondrej, J; O'Sullivan, C (2017)
Publisher: Institute of Electrical and Electronics Engineers
Languages: English
Types: Article
Subjects:
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [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.
    • [21] S. Singh, M. Kapadia, P. Faloutsos, and G. Reinman, “SteerBench: a Benchmark Suite for Evaluating Steering Behaviors,” Comp. Anim. Virt. Worlds, vol. 20, no. 5-6, pp. 533-548, 2009.
    • [22] E. Ju, M. G. Choi, M. Park, J. Lee, K. H. Lee, and S. Takahashi, “Morphable Crowds,” ACM Trans. Graph., vol. 29, no. 6, pp. 140:1- 140:10, 2010.
    • [23] M. Kapadia, M. Wang, S. Singh, G. Reinman, and P. Faloutsos, “Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms,” in SCA, 2011, pp. 53-62.
    • [24] S. R. Musse, V. J. Cassol, and C. R. Jung, “Towards a Quantitative Approach for Comparing Crowds,” Comp. Anim. Virt. Worlds, vol. 23, no. 1, pp. 49-57, 2012.
    • [25] D. Wolinski, S. J. Guy, A.-H. Olivier, M. C. Lin, D. Manocha, and J. Pettre´, “Parameter Estimation and Comparative Evaluation of Crowd Simulations,” Comp. Graph. Forum, vol. 33, no. 2, pp. 303- 312, 2014.
    • [26] S. J. Guy, J. van den Berg, W. Liu, R. Lau, M. C. Lin, and D. Manocha, “A Statistical Similarity Measure for Aggregate Crowd Dynamics,” ACM Trans. Graph., vol. 31, no. 6, pp. 190:1- 190:11, 2012.
    • [27] A. Lerner, Y. Chrysanthou, A. Shamir, and D. Cohen-Or, “Data Driven Evaluation of Crowds,” in Motion in Games, 2009, pp. 75- 83.
    • [28] P. Charalambous, I. Karamouzas, S. J. Guy, and Y. Chrysanthou, “A Data-Driven Framework for Visual Crowd Analysis,” Comp. Graph. Forum, vol. 33, no. 7, pp. 41-50, 2014.
    • [29] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
    • [30] Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, “Hierarchical Dirichlet Processes,” J. Am. Stat. Assoc., vol. 101, no. 476, pp. 1566- 1581, 2006.
    • [31] H. Wang and C. O'Sullivan, “Globally Continuous and nonMarkovian Crowd Activity Analysis from Videos,” in ECCV, 2016, pp. 527-544.
    • [32] L. Fei-Fei and P. Perona, “A Bayesian Hierarchical Model for Learning Natural Scene Categories,” in CVPR, 2005, pp. 524-531.
    • [33] E. B. Sudderth, A. Torralba, W. T. Freeman, and A. S. Willsky, “Describing Visual Scenes Using Transformed Objects and Parts,” Int J Comput Vis., vol. 77, no. 1-3, pp. 291-330, 2007.
    • [34] J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering Object Categories in Image Collections,” ICCV, 2005.
    • [35] J. C. Niebles, H. Wang, and L. Fei-Fei, “Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words,” Int. J. Comp. Vision, vol. 79, no. 3, pp. 299-318, 2008.
    • [36] L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience, 2005.
    • [37] B. Zhou, X. Wang, and X. Tang, “Random Field Topic Model for Semantic Region Analysis in Crowded Scenes from Tracklets,” in CVPR, 2011, pp. 3441-3448.
    • [38] X. Wang, X. Ma, and W. Grimson, “Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models,” IEEE Trans. Patt. Anal. Machine Intel., vol. 31, no. 3, pp. 539-555, 2009.
    • [39] B. Zhou, X. Wang, and X. Tang, “Understanding Collective Crowd Behaviors: Learning a Mixture Model of Dynamic Pedestrianagents,” in CVPR, 2012, pp. 2871-2878.
    • [40] T. Ikeda, Y. Chigodo, D. Rea, F. Zanlungo, M. Shiomi, and T. Kanda, “Modeling and Prediction of Pedestrian Behavior Based on the Sub-goal Concept,” Robotics, p. 137, 2013.
    • [41] S. Ali and M. Shah, “A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis,” in CVPR, Jun. 2007, pp. 1-6.
    • [42] J. Zhong, W. Cai, L. Luo, and H. Yin, “Learning Behavior Patterns from Video: A Data-driven Framework for Agent-based Crowd Modeling,” in Autonomous Agents and Multiagent Systems, 2015, pp. 801-809.
    • [43] J. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” in Berkeley Symp. on Math. Statist. and Prob., 1967, pp. 281-297.
    • [44] C. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2007.
    • [45] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, 2000.
    • [46] Y. W. Teh, K. Kurihara, and M. Welling, “Collapsed Variational Inference for HDP,” in NIPS, 2008.
    • [47] M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley, “Stochastic Variational Inference,” J. Mach. Learn. Res., vol. 14, no. 1, pp. 1303- 1347, 2013.
    • [48] C. Wang, J. Paisley, and D. M. Blei, “Online Variational Inference for the Hierarchical Dirichlet Process,” in AISTATS, 2011.
    • [49] M. Moussad, D. Helbing, S. Garnier, A. Johansson, M. Combe, and G. Theraulaz, “Experimental Study of the Behavioural Mechanisms Underlying Self-organization in Human Crowds,” Proc. Biol. Sci., vol. 276, no. 1668, pp. 2755-2762, 2009.
    • [50] S. Paris, J. Pettre´, and S. Donikian, “Pedestrian Reactive Navigation for Crowd Simulation: a Predictive Approach,” Comp. Graph. Forum, vol. 26, no. 3, pp. 665-674, 2007.
    • [51] J. v. d. Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-Body Collision Avoidance,” in Robotics Research, 2011, no. 70, pp. 3-19.
    • [52] G. Snook, “Simplified 3d Movement and Pathfinding Using Navigation Meshes,” in Game Programming Gems, M. DeLoura, Ed., 2000, pp. 288-304.
    • [53] J.-C. Latombe, Robot Motion Planning. Norwell, MA, USA: Kluwer Academic Publishers, 1991.
    • [54] O. Khatib, “Real-time Obstacle Avoidance for Manipulators and Mobile Robots,” in IEEE ICRA, vol. 2, Mar. 1985, pp. 500-505.
    • [55] S. Curtis, A. Best, and D. Manocha, “Menge: A Modular Framework for Simulating Crowd Movement,” University of North Carolina at Chapel Hill, Tech. Rep, 2014.
    • [56] F. Lamarche and S. Donikian, “Crowd of Virtual Humans: A New Approach for Real Time Navigation in Complex and Structured Environments,” Comp. Graph. Forum, vol. 23, no. 3, pp. 509-518, 2004.
    • [57] M. Kapadia, M. Wang, G. Reinman, and P. Faloutsos, “Improved Benchmarking for Steering Algorithms,” in Motion in Games, 2011, pp. 266-277.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article