Pedestrian Travel Time Estimation in Crowded Scenes

In this paper, we target on the problem of estimating the statistic of pedestrian travel time within a period from an entrance to a destination in a crowded scene. Such estimation is based on the global distributions of crowd densities and velocities instead of complete trajectories of pedestrians, which cannot be obtained in crowded scenes. The proposed method is motivated by our statistical investigation into the correlations between travel time and global properties of crowded scenes. Active regions are created for each source-destination pair to model the probable walking regions over the corresponding source-destination traffic flow. Two sets of scene features are specially designed for modeling moving and stationary persons inside the active regions and their influences on pedestrian travel time. The estimation of pedestrian travel time provides valuable information for both crowd scene understanding and pedestrian behavior analysis, but was not sufficiently studied in literature. The effectiveness of the proposed pedestrian travel time estimation model is demonstrated through several surveillance applications, including dynamic scene monitoring, localization of regions blocking traffics, and detection of abnormal pedestrian behaviors. Many more valuable applications based on our method are to be explored in the future.

[1]  Xiaogang Wang,et al.  Random field topic model for semantic region analysis in crowded scenes from tracklets , 2011, CVPR 2011.

[2]  J. Gillon,et al.  Group dynamics , 1996 .

[3]  Mubarak Shah,et al.  Scene understanding by statistical modeling of motion patterns , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[5]  W. Eric L. Grimson,et al.  Unsupervised Activity Perception by Hierarchical Bayesian Models , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Mubarak Shah,et al.  Learning object motion patterns for anomaly detection and improved object detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Mubarak Shah,et al.  Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[10]  Robert T. Collins,et al.  Marked point processes for crowd counting , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Xiaogang Wang,et al.  Understanding pedestrian behaviors from stationary crowd groups , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Tim J. Ellis,et al.  Learning semantic scene models from observing activity in visual surveillance , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Xiaogang Wang,et al.  Profiling stationary crowd groups , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[14]  Qi Tian,et al.  Query-adaptive late fusion for image search and person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[16]  Ian D. Reid,et al.  Stable multi-target tracking in real-time surveillance video , 2011, CVPR 2011.

[17]  Xiaogang Wang,et al.  Deeply learned attributes for crowded scene understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Mubarak Shah,et al.  Video Scene Understanding Using Multi-scale Analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Xiaogang Wang,et al.  Cross-scene crowd counting via deep convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Xiaogang Wang,et al.  Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Antoni B. Chan,et al.  Crossing the Line: Crowd Counting by Integer Programming with Local Features , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Nuno Vasconcelos,et al.  Counting People With Low-Level Features and Bayesian Regression , 2012, IEEE Transactions on Image Processing.

[23]  Dinesh Manocha,et al.  Leveraging Long-Term Predictions and Online Learning in Agent-Based Multiple Person Tracking , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Shaogang Gong,et al.  Cumulative Attribute Space for Age and Crowd Density Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Xiaogang Wang,et al.  Scene-Independent Group Profiling in Crowd , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[28]  G. L. Bon,et al.  Scientific Literature: The Crowd. A Study of the Popular Mind , 1897 .

[29]  Tianzhu Zhang,et al.  Learning semantic scene models by object classification and trajectory clustering , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Xiaogang Wang,et al.  Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents , 2014, International Journal of Computer Vision.

[31]  Mubarak Shah,et al.  Abnormal crowd behavior detection using social force model , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Marshall F. Tappen,et al.  Learning pedestrian dynamics from the real world , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  W. Eric L. Grimson,et al.  Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Michel Bierlaire,et al.  Discrete Choice Models for Pedestrian Walking Behavior , 2006 .

[35]  Zhongke Shi,et al.  Toward Dynamic Scene Understanding by Hierarchical Motion Pattern Mining , 2014, IEEE Transactions on Intelligent Transportation Systems.

[36]  Mubarak Shah,et al.  Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Jin Li,et al.  Crowd Density and Counting Estimation Based on Image Textural Feature , 2014, J. Multim..

[38]  Gérard G. Medioni,et al.  Robust unsupervised motion pattern inference from video and applications , 2011, 2011 International Conference on Computer Vision.

[39]  Xiaogang Wang,et al.  L0 Regularized Stationary Time Estimation for Crowd Group Analysis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.