People detection and tracking using stereo vision and color

  • Authors:

  • Rafael Muñoz-Salinas;Eugenio Aguirre;Miguel García-Silvente

  • Affiliations:

  • Department of Computing and Numerical Analysis, University of Córdoba, 14071 Córdoba, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2007

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Abstract

People detection and tracking are important capabilities for applications that desire to achieve a natural human-machine interaction. Although the topic has been extensively explored using a single camera, the availability and low price of new commercial stereo cameras makes them an attractive sensor to develop more sophisticated applications that take advantage of depth information. This work presents a system able to visually detect and track multiple people using a stereo camera placed at an under-head position. This camera position is especially appropriated for human-machine applications that require interacting with people or to analyze human facial gestures. The system models the background as height map that is employed to easily extract foreground objects among which people are found using a face detector. Once a person has been spotted, the system is capable of tracking him while is still looking for more people. Our system tracks people combining color and position information (using the Kalman filter). Tracking based exclusively on position information is unreliable when people establish close interactions. Thus, we also include color information about the people clothes in order to increase the tracking robustness. The system has been extensively tested and the results show that the use of color greatly reduces the errors of the tracking system. Besides, the people detection technique employed, based on combining plan-view map information and a face detector, has proved in our experimentation to avoid false detections in the tests performed. Finally, the low computing time required for the detection and tracking process makes it suitable to be employed in real time applications.