Paper Info
Title
Uncertainty Estimation For Klt Tracking
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.
Year
DOI
Venue
2014
10.1007/978-3-319-16631-5_35
COMPUTER VISION - ACCV 2014 WORKSHOPS, PT II
Field
DocType
Volume
Structure from motion,Computer vision,Monte Carlo method,Computer science,Image stabilization,Image noise,Maxima and minima,Artificial intelligence,Simultaneous localization and mapping,Mixture model,Covariance
Conference
9009
ISSN
Citations 
PageRank 
0302-9743
1
0.35
References 
Authors
6
5
Name
Order
Citations
PageRank
Sheorey, Sameer1926.84
Shalini Keshavamurthy210.69
Huili Yu3304.26
Hieu T. Nguyen432716.41
Clark N. Taylor517823.82