Going with the Flow: Pedestrian Efficiency in Crowded Scenes

Video analysis of crowded scenes is challenging due to the complex motion of individual people in the scene. The collective motion of pedestrians form a crowd flow, but individuals often largely deviate from it as they anticipate and react to each other. Deviations from the crowd decreases the pedestrian's efficiency: a sociological concept that measures the difference of actual motion from the intended speed and direction. In this paper, we derive a novel method for estimating pedestrian efficiency from videos. We first introduce a novel crowd motion model that encodes the temporal evolution of local motion patterns represented with directional statistics distributions. This model is then used to estimate the intended motion of pedestrians at every space-time location, which enables visual measurement of the pedestrian efficiency. We demonstrate the use of this pedestrian efficiency to detect unusual events and to track individuals in crowded scenes. Experimental results show that the use of pedestrian efficiency leads to state-of-the-art accuracy in these critical applications.

[1]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Alessio Del Bue,et al.  Optimizing interaction force for global anomaly detection in crowded scenes , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[3]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[4]  Nuno Vasconcelos,et al.  Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Ko Nishino,et al.  Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  L. F. Henderson,et al.  The Statistics of Crowd Fluids , 1971, Nature.

[7]  S. R. Jammalamadaka,et al.  Directional Statistics, I , 2011 .

[8]  Serge P. Hoogendoorn,et al.  Pedestrian Behavior at Bottlenecks , 2005, Transp. Sci..

[9]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[10]  Kanti V. Mardia,et al.  Bayesian inference for the von Mises-Fisher distribution , 1976 .

[11]  Ramin Mehran,et al.  Abnormal crowd behavior detection using social force model , 2009, CVPR.

[12]  Dirk Helbing,et al.  Optimal self-organization , 1999 .

[13]  Robert Pless,et al.  Analysis of Persistent Motion Patterns Using the 3D Structure Tensor , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[14]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[15]  M. Schreckenberg,et al.  Experimental study of pedestrian counterflow in a corridor , 2006, cond-mat/0609691.

[16]  Hubert Klüpfel,et al.  Evacuation Dynamics: Empirical Results, Modeling and Applications , 2009, Encyclopedia of Complexity and Systems Science.

[17]  Qian Yu,et al.  Motion pattern interpretation and detection for tracking moving vehicles in airborne video , 2009, CVPR.

[18]  Mubarak Shah,et al.  Floor Fields for Tracking in High Density Crowd Scenes , 2008, ECCV.

[19]  Axel Pinz,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[20]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .

[21]  Takeo Kanade,et al.  Tracking in unstructured crowded scenes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  William Chauvenet,et al.  A manual of spherical and practical astronomy , 1891 .

[23]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Mubarak Shah,et al.  Visual crowd surveillance through a hydrodynamics lens , 2011, Commun. ACM.

[25]  Dinesh Manocha,et al.  PLEdestrians: a least-effort approach to crowd simulation , 2010, SCA '10.

[26]  Mubarak Shah,et al.  A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[28]  Anil M. Cheriyadat,et al.  Detecting Dominant Motions in Dense Crowds , 2008, IEEE Journal of Selected Topics in Signal Processing.

[29]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[30]  Louis Kratz,et al.  Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, CVPR.

[31]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[32]  Christian Bauckhage,et al.  Analyzing pedestrian behavior in crowds for automatic detection of congestions , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[33]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.