Home/A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video

A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video

Hulya Yalcin, Robert Collins, Martial Hebert and Michael J. Black
Carnegie Mellon University, Video Proceedings in conjunction with CVPR'05, June, 2005

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

BibTeX Reference
@conference{Yalcin-2005-9190,
title = {A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video},
author = {Hulya Yalcin and Robert Collins and Martial Hebert and Michael J. Black},
booktitle = {Video Proceedings in conjunction with CVPR'05},
keyword = {motion estimation, tracking, background estimation},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2005},
address = {Pittsburgh, PA},
}
2017-09-13T10:43:25+00:00