A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video
Hulya Yalcin, Robert Collins, Michael J. Black, Martial Hebert


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.

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Carnegie Mellon University, Robotics Institute
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hulya@ri.cmu.edu