A novel framework for automated monitoring and analysis of high density pedestrian flow

Pedestrian traffic is an important subject of surveillance to ensure public safety and traffic management, which may benefit from intelligent and continuous analysis of pedestrian videos. State-of-the-art methods for intelligent pedestrian surveillance have a number of limitations in automating and deriving useful information of high-density pedestrian traffic (HDPT) using closed circuit television (CCTV) images. This work introduces an automatic and improved HDPT surveillance system by integrating and optimizing multiple computational steps to predict pedestrian distribution from input video frames. A fast and efficient particle image velocimetry (PIV) technique is proposed to yield pedestrian velocities. A machine learning regressor model, boosted Ferns, is used to improve pedestrian count and density estimation: an essential metric for HDPT analysis. A camera perspective model is proposed to improve the speed and position estimates of HDPT by projecting 2D image pixels to 3D world-coordinate dat. All these functional improvements in HDPT velocity and displacement estimations are used as inputs to a sophisticated pedestrian flow evolution model, PEDFLOW to predict HDPT distribution at a future time point, which is a crucial information for pedestrian traffic management. The predicted and simulated HDPT properties (density, velocity) obtained using the proposed framework show low errors when compared to the ground truth data. The proposed framework is computationally efficient, suitable for multiple camera feeds with HDPT videos, and capable of rapidly analyzing and predicting flows of thousands of pedestrians. The paper shows one of the first steps towards fully integrated CCTV-based automated HDPT management system.

• Record URL:
  https://doi.org/10.1080/15472450.2019.1643724
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
• Supplemental Notes:
  • © 2019 Taylor & Francis Group, LLC. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Baqui, Muhammad
  • Samad, Manar D
  • Löhner, Rainald
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; Photos; References; Tables;
• Pagination: pp 585-597
• Serial:
  • Journal of Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 6
  • Publisher: Taylor & Francis
  • ISSN: 1547-2450
  • EISSN: 1547-2442
  • Serial URL: http://www.tandfonline.com/loi/gits20

Subject/Index Terms

• TRT Terms: Automation; Crowds; Intelligent transportation systems; Pedestrian counts; Pedestrian flow; Traffic density; Traffic surveillance; Video
• Subject Areas: Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01759163
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 30 2020 11:12AM