A Computer Vision Tool Set for Innovative Elder Pedestrians Aware Crowd Management Support Systems

As the population of world is increasing, and even more concentrated in urban areas, ensuring public safety is becoming a taunting job for security personnel and crowd managers. Mass events like sports, festivals, concerts, political gatherings attract thousand of people in a constrained environment, therefore adequate safety measures should be adopted. The ageing of the population further increases the urgency of computer supported crowd management support systems especially considering the fragility of elder pedestrians. In recent years, researchers developed several models for simulating crowd dynamics. These models should be properly calibrated and validated by means of data acquired in the field. In this paper, we will describe a computer vision tool set that can provide support to information needs of an integrated crowd management support system.

[1]  Afshin Dehghan,et al.  GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs , 2012, ECCV.

[2]  Stefania Bandini,et al.  When reactive agents are not enough: Tactical level decisions in pedestrian simulation , 2015, Intelligenza Artificiale.

[3]  Stefania Bandini,et al.  Detecting Dominant Motion Flows and People Counting in High Density Crowds , 2014, J. WSCG.

[4]  Kiyoharu Aizawa,et al.  Detecting Dominant Motion Flows in Unstructured/Structured Crowd Scenes , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Saleh Basalamah,et al.  Counting of People in the Extremely Dense Crowd using Genetic Algorithm and Blobs Counting , 2013 .

[6]  Stefania Bandini,et al.  Analyzing crowd behavior in naturalistic conditions: Identifying sources and sinks and characterizing main flows , 2016, Neurocomputing.

[7]  Edward J. Delp,et al.  Crowd flow estimation using multiple visual features for scenes with changing crowd densities , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[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]  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]  Stefania Bandini,et al.  Simulation and Evaluation of Spiral Movement of Pedestrians: Towards the Tawaf Simulator , 2016, J. Cell. Autom..

[11]  Norbert Brändle,et al.  Evaluation of clustering methods for finding dominant optical flow fields in crowded scenes , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Nasim A Khan,et al.  Pattern of medical diseases and determinants of prognosis of hospitalization during 2005 Muslim pilgrimage Hajj in a tertiary care hospital. A prospective cohort study. , 2006, Saudi medical journal.

[13]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

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