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2015 | OriginalPaper | Chapter

Uncertainty Estimation for KLT Tracking

Authors : Sameer Sheorey, Shalini Keshavamurthy, Huili Yu, Hieu Nguyen, Clark N. Taylor

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

The Kanade-Lucas-Tomasi tracker (KLT) is commonly used for tracking feature points due to its excellent speed and reasonable accuracy. It is a standard algorithm in applications such as video stabilization, image mosaicing, egomotion estimation, structure from motion and Simultaneous Localization and Mapping (SLAM). However, our understanding of errors in the output of KLT tracking is incomplete. In this paper, we perform a theoretical error analysis of KLT tracking. We first focus our analysis on the standard KLT tracker and then extend it to the pyramidal KLT tracker and multiple frame tracking. We show that a simple local covariance estimate is insufficient for error analysis and a Gaussian Mixture Model is required to model the multiple local minima in KLT tracking. We perform Monte Carlo simulations to verify the accuracy of the uncertainty estimates.

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Metadata
Title
Uncertainty Estimation for KLT Tracking
Authors
Sameer Sheorey
Shalini Keshavamurthy
Huili Yu
Hieu Nguyen
Clark N. Taylor
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16630-8

Electronic ISBN: 978-3-319-16631-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16631-5_35

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