Learning to Track Multiple People in Omnidirectional Video - Robotics Institute Carnegie Mellon University

Learning to Track Multiple People in Omnidirectional Video

Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, pp. 4150 - 4155, April, 2005

Abstract

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.

BibTeX

@conference{Frade-2005-9143,
author = {Fernando De la Torre Frade and Carlos Vallespi-Gonzalez and Paul Rybski and Manuela Veloso and Takeo Kanade},
title = {Learning to Track Multiple People in Omnidirectional Video},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2005},
month = {April},
pages = {4150 - 4155},
keywords = {Omnidirectional-video capturing, Multiple people tracking, Subspace methods, Meeting understanding, Person-specific models.},
}