Carnegie Mellon University
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Learning to Track Multiple People in Omnidirectional Video

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journal contribution
posted on 2005-01-01, 00:00 authored by Fernando De la Torre, Carlos Vallespi, Paul E. Rybski, Manuela Veloso, Takeo Kanade
Meetings are a very important part of everyday life for professionals working in universities, companies or governmental institutions.We have designed a physical awareness system called CAMEO (Camera Assisted Meeting Event Observer), a hardware/software system to record and monitor people’s activities in meetings. CAMEO captures a high resolution omnidirectional view of the meeting by stitching images coming from almost concentric cameras. Besides recording capability, CAMEO automatically detects people and learns a person-specific facial appearance model (PSFAM) for each of the participants. The PSFAMs allow more robust/reliable tracking and identification. In this paper, we describe the video-capturing device, photometric/geometric autocalibration process, and the multiple people tracking system. The effectiveness and robustness of the proposed system is demonstrated over several real-time experiments and a large data set of videos.


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