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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Acknowledgement
This research was supported by Air Force Research Laboratory (AFRL) under contract FA8650-13-M-1701 with UtopiaCompression Corporation.
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Sheorey, S., Keshavamurthy, S., Yu, H., Nguyen, H., Taylor, C.N. (2015). Uncertainty Estimation for KLT Tracking. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_35
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DOI: https://doi.org/10.1007/978-3-319-16631-5_35
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