Change Detection in Human Crowds

This paper presents a method to detect unusual behavior in human crowds based on histograms of velocities in world coordinates. A combination of background removal and optical flow is used to extract the global motion at each image frame, discarding small motion vectors due artifacts such as noise, non-stationary background pixels and compression issues. Using a calibrated camera, the global motion can be estimated, and it is used to build a 2D histogram containing information of speed and direction for all frames. Each frame is compared with a set of previous frames by using a histogram comparison metric, resulting in a similarity vector. This vector is then used to determine changes in the crowd behavior, also allowing a classification based on the nature of the change in time: short or long-term changes. The method was tested on publicly available datasets involving crowded scenarios.

[1]  Roberto Cipolla,et al.  Unsupervised Bayesian Detection of Independent Motion in Crowds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Yangsheng Xu,et al.  Abnormal crowd motion analysis , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[3]  Alice Caplier,et al.  Crowd behaviour analysis using histograms of motion direction , 2010, 2010 IEEE International Conference on Image Processing.

[4]  Sergio A. Velastin,et al.  Crowd analysis: a survey , 2008, Machine Vision and Applications.

[5]  Yoav Meckel,et al.  Preferred transition speed between walking and running: effects of training status. , 2005, Medicine and science in sports and exercise.

[6]  Mubarak Shah,et al.  Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Ehud Rivlin,et al.  Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[9]  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.

[10]  Wei Li,et al.  Crowd movement segmentation using velocity field histogram curve , 2012, 2012 International Conference on Wavelet Analysis and Pattern Recognition.

[11]  Fredrik Gustafsson,et al.  Recognition of Anomalous Motion Patterns in Urban Surveillance , 2013, IEEE Journal of Selected Topics in Signal Processing.

[12]  Jenq-Neng Hwang,et al.  Nonparametric multivariate density estimation: a comparative study , 1994, IEEE Trans. Signal Process..

[13]  Yiannis Kompatsiaris,et al.  Spatiotemporally localized new event detection in crowds , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[14]  Cláudio Rosito Jung,et al.  Efficient Background Subtraction and Shadow Removal for Monochromatic Video Sequences , 2009, IEEE Transactions on Multimedia.

[15]  W. Grimson,et al.  Ground Plane Rectification by Tracking Moving Objects , 2003 .

[16]  Tieniu Tan,et al.  Practical Camera Calibration From Moving Objects for Traffic Scene Surveillance , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Chabane Djeraba,et al.  Real-time crowd motion analysis , 2008, 2008 19th International Conference on Pattern Recognition.

[18]  Duan-Yu Chen,et al.  Motion-based unusual event detection in human crowds , 2011, J. Vis. Commun. Image Represent..

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

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

[21]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[22]  M. Manzur Murshed,et al.  Panic-driven event detection from surveillance video stream without track and motion features , 2010, 2010 IEEE International Conference on Multimedia and Expo.