Robust Estimation of Parameters for Lucas-Kanade Algorithm

DOI

10.2991/icmt-13.2013.114How to use a DOI?

Keywords

Lucas-Kanade algorithm Least trimmed squares

Abstract

The object tracking problem is an important research topic in computer vision. For real applications such as vehicle tracking and face tracking, there are many efficient and real-time algorithms. In this study, we will focus on the Lucas-Kanade (LK) algorithm for object tracking. In the standard LK method, sum of squared errors is used as the cost function, while least trimmed squares is adopted as the cost function in this study. The resulting estimator is robust against outliers caused by noises in the tracking process. Simulation is provided to show that the proposed algorithm outperforms the standard LK method in the sense that it is robust against the outliers in the object tracking problem.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lin Yih-Lon
PY  - 2013/11
DA  - 2013/11
TI  - Robust Estimation of Parameters for Lucas-Kanade Algorithm
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 919
EP  - 926
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.114
DO  - 10.2991/icmt-13.2013.114
ID  - Yih-Lon2013/11
ER  -