A novel method for tracking and counting pedestrians in real-time using a single camera

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This paper presents a real-time system for pedestrian tracking in sequences of grayscale images acquired by a stationary camera. The objective is to integrate this system with a traffic control application such as a pedestrian control scheme at intersections. The proposed approach can also be used to detect and track humans in front of vehicles. Furthermore, the proposed schemes can be employed for the detection of several diverse traffic objects of interest (vehicles, bicycles, etc.) The system outputs the spatio-temporal coordinates of each pedestrian during the period the pedestrian is in the scene. Processing is done at three levels: raw images, blobs, and pedestrians. Blob tracking is modeled as a graph optimization problem. Pedestrians are modeled as rectangular patches with a certain dynamic behavior. Kalman filtering is used to estimate pedestrian parameters. The system was implemented on a Datacube MaxVideo 20 equipped with a Datacube Max860 and was able to achieve a peak performance of over 30 frames per second. Experimental results based on indoor and outdoor scenes demonstrated the system's robustness under many difficult situations such as partial or full occlusions of pedestrians.

Original languageEnglish (US)
Pages (from-to)1267-1278
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Issue number5
StatePublished - Sep 2001

Bibliographical note

Funding Information:
Manuscript received April 22, 1997; revised January 28, 2001. This work was supported by the Minnesota Department of Transportation through Contracts #71 789-72 983-169 and #71 789-72 447-159, the Center for Transportation Studies through Contract #USDOT/DTRS 93-G-0017-01, the National Science Foundation through Contracts #IRI-9 410 003 and #IRI-9502245, the Department of Energy (Sandia National Laboratories) through Contracts #AC-3752D and #AL-3021, and the McKnight Land-Grant Professorship Program at the University of Minnesota.

Funding Information:
Dr. Masoud is a recipient of the Research Contribution Award from the University of Minnesota, the Rosemount Instrumentation Award from Rosemount Inc., and the Matt Huber Award for Excellence in Transportation Research.


  • Applications
  • Image sequence analysis
  • Intelligent transportation systems
  • Pedestrian control at intersections
  • Pedestrian tracking
  • Real-time tracking


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