Automated multi-camera planar tracking correspondence modeling

Authors:
Chris Stauffer;Kinh Tieu
Affiliations:
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA
Venue:
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Year:
2003

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Abstract

This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera network directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our method can reliably estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Quantitative results are shown for a five camera network in which the topology is not specified.