Crowd behaviour monitoring on the escalator exits

This paper presents an approach to detect an abnormal situation in a crowd scene. The proposed approach estimates sudden changes and abnormal motion variations in a set of interest points. The number of tracked points of interest is reduced by using a mask that corresponds to the hot areas of the built motion heat map. Optical flow technique tracks the points of interest. There are sufficient variations in the optical flow patterns in a crowd scene when there are cases those showing abnormal situations. Statistical treatment of optical flow information has been thresholded. To demonstrate the interest of this approach, we present the results based on the detection of collapsing events in real videos of airport escalator exits.

[1]  Robert B. Fisher,et al.  Modelling Crowd Scenes for Event Detection , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[3]  Hans P. Moravec.  Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[4]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[5]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[6]  Qi Tian,et al.  Foreground object detection from videos containing complex background , 2003, MULTIMEDIA '03.

[7]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[8]  L. Li,et al.  On pixel count based crowd density estimation for visual surveillance , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[9]  Michal Irani,et al.  Detecting Irregularities in Images and in Video , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Sheng-Fuu Lin,et al.  Estimation of number of people in crowded scenes using perspective transformation , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[11]  M. Nixon,et al.  On crowd density estimation for surveillance , 2006 .

[12]  A. Marana,et al.  Estimation of crowd density using image processing , 1997 .

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

[14]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[16]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[17]  Robert B. Fisher,et al.  Hidden Markov Models for Optical Flow Analysis in Crowds , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[18]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[19]  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.

[20]  Chia-Fen Chi,et al.  In-depth investigation of escalator riding accidents in heavy capacity MRT stations. , 2006, Accident; analysis and prevention.