Detecting interaction/complexity within crowd movements using braid entropy

The segmentation of moving and non-moving regions in an image within the field of crowd analysis is a crucial process in terms of understanding crowd behavior. In many studies, similar movements were segmented according to the location, adjacency to each other, direction, and average speed. However, these segments may not in turn indicate the same types of behavior in each region. The purpose of this study is to better understand crowd behavior by locally measuring the degree of interaction/complexity within the segment. For this purpose, the flow of motion in the image is primarily represented as a series of trajectories. The image is divided into hexagonal cells and the finite time braid entropy (FTBE) values are calculated according to the different projection angles of each cell. These values depend on the complexity of the spiral structure that the trajectories generated throughout the movement and show the degree of interaction among pedestrians. In this study, behaviors of different complexities determined in segments are pictured as similar movements on the whole. This study has been tested on 49 different video sequences from the UCF and CUHK databases.

[1]  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.

[2]  Mubarak Shah,et al.  A Streakline Representation of Flow in Crowded Scenes , 2010, ECCV.

[3]  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.

[4]  Soraia Raupp Musse,et al.  Crowd Analysis Using Computer Vision Techniques , 2010, IEEE Signal Processing Magazine.

[5]  Shuang Wu,et al.  Crowd Behavior Analysis via Curl and Divergence of Motion Trajectories , 2017, International Journal of Computer Vision.

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

[7]  Günter Bärwolff,et al.  A derived grid-based model for simulation of pedestrian flow , 2009 .

[8]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Bolei Zhou,et al.  Measuring Crowd Collectiveness , 2013, CVPR.

[10]  Yixiong Feng,et al.  Simulation model of self-organizing pedestrian movement considering following behavior , 2017, Frontiers of Information Technology & Electronic Engineering.

[11]  Jun Jiang,et al.  Crowd motion segmentation via streak flow and collectiveness , 2017, 2017 Chinese Automation Congress (CAC).

[12]  Xiaogang Wang,et al.  Learning Scene-Independent Group Descriptors for Crowd Understanding , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Xiaofei Wang,et al.  A high accuracy flow segmentation method in crowded scenes based on streakline , 2014 .

[14]  Guofeng Su,et al.  Crowd macro state detection using entropy model , 2015 .

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

[16]  Marko Budisic,et al.  Braidlab: A Software Package for Braids and Loops , 2014, 1410.0849.

[17]  Marimuthu Palaniswami,et al.  Crowd Event Detection on Optical Flow Manifolds , 2016, IEEE Transactions on Cybernetics.

[18]  Xuelong Li,et al.  Anchor-based group detection in crowd scenes , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Mubarak Shah,et al.  Learning motion patterns in crowded scenes using motion flow field , 2008, 2008 19th International Conference on Pattern Recognition.

[20]  How-Lung Eng,et al.  A Literature Review on Video Analytics of Crowded Scenes , 2013, Intelligent Multimedia Surveillance.

[21]  Tao Mei,et al.  A Diffusion and Clustering-Based Approach for Finding Coherent Motions and Understanding Crowd Scenes , 2016, IEEE Transactions on Image Processing.

[22]  Finding Lagrangian Structures via the Application of Braid Theory , 2010 .

[23]  Saad Ali.  Measuring Flow Complexity in Videos , 2013, 2013 IEEE International Conference on Computer Vision.

[24]  H. Bhaskar,et al.  Advances and trends in visual crowd analysis: A systematic survey and evaluation of crowd modelling techniques , 2016, Neurocomputing.

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

[26]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[27]  Xiaogang Wang,et al.  Coherent Filtering: Detecting Coherent Motions from Crowd Clutters , 2012, ECCV.

[28]  Zhiwen Liu,et al.  Adaptive Crowd Segmentation Based on Coherent Motion Detection , 2017, Journal of Signal Processing Systems.

[29]  Jean-Luc Thiffeault,et al.  Detecting coherent structures using braids , 2011, 1106.2231.

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

[31]  Jacques-Olivier Moussafir.  On computing the entropy of braids , 2006 .

[32]  Pierre-Marc Jodoin,et al.  Meta-tracking for video scene understanding , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

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

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

[35]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[36]  Quoc Cuong Pham,et al.  Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

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

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

[39]  Mubarak Shah,et al.  Detecting global motion patterns in complex videos , 2008, 2008 19th International Conference on Pattern Recognition.

[40]  Norman I. Badler,et al.  Detection of Global and Local Motion Changes in Human Crowds , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[41]  Bingbing Ni,et al.  Crowded Scene Analysis: A Survey , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[42]  Yu Yang,et al.  A review of behavior mechanisms and crowd evacuation animation in emergency exercises , 2013, Journal of Zhejiang University SCIENCE C.