Using automated walking gait analysis for the identification of pedestrian attributes

Collecting microscopic pedestrian behavior and characteristics data is important for optimizing the design of pedestrian facilities for safety, efficiency, and comfortability. This paper provides a framework for the automated classification of pedestrian attributes such as age and gender based on information extracted from their walking gait behavior. The framework extends earlier work on the automated analysis of gait parameters to include analysis of the gait acceleration data which can enable the quantification of the variability, rhythmic pattern and stability of pedestrian’s gait. In this framework, computer vision techniques are used for the automatic detection and tracking of pedestrians in an open environment resulting in pedestrian trajectories and the speed and acceleration dynamic profiles. A collection of gait features are then derived from those dynamic profiles and used for the classification of pedestrian attributes. The gait features include conventional gait parameters such as gait length and frequency and dynamic parameters related to gait variations and stability measures. Two different techniques are used for the classification: a supervised k-Nearest Neighbors (k-NN) algorithm and a newly developed semi-supervised spectral clustering. The classification framework is demonstrated with two case studies from Vancouver, British Columbia and Oakland, California. The results show the superiority of features sets including gait variations and stability measures over features relying only on conventional gait parameters. For gender, correct classification rates (CCR) of 80% and 94% were achieved for the Vancouver and Oakland case studies, respectively. The classification accuracy for gender was higher in the Oakland case which only considered pedestrians walking alone. Pedestrian age classification resulted in a CCR of 90% for the Oakland case study.

• Record URL:
  • https://doi.org/10.1016/j.trc.2014.08.004
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X14002204
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Zaki, Mohamed H
  • Sayed, Tarek
• Publication Date: 2014-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References; Tables;
• Pagination: pp 16-36
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 48
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Age; Computer vision; Gender; Human characteristics; Pedestrian detectors; Pedestrians; Video imaging detectors; Walking
• Uncontrolled Terms: Pedestrian gait analysis
• Geographic Terms: Oakland (California); Vancouver (Canada)
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Pedestrians and Bicyclists; Society; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01547429
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 9 2014 8:38AM