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Carnegie Mellon Robotics Institute

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Abstract

In this work, we address the detection of vehicles in a video stream obtained from a moving airborne platform. We propose a Bayesian framework for estimating dense optical flow over time that explicitly estimates a persistent model of background appearance. The approach assumes that the scene can be described by background and occlusion layers, estimated within an Expectation-Maximization framework. The mathematical formulation of the paper is an extension of our previous work where motion and appearance models for foreground and background layers are estimated simultaneously in a Bayesian framework.

Keywords

motion estimation, tracking, background estimation

Notes

Number of pages: 1

Text Reference

Hulya Yalcin, Robert Collins, Martial Hebert, and Michael J. Black, "A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video," Video Proceedings in conjunction with CVPR'05, June, 2005.

BibTeX Reference

@inproceedings{Yalcin_2005_5018,
   author = "Hulya Yalcin and Robert Collins and Martial Hebert and Michael J. Black",
   title = "A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video",
   booktitle = "Video Proceedings in conjunction with CVPR'05",
   month = "June",
   year = "2005",
}

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