Robust Estimation of Parameters for Lucas-Kanade Algorithm
Available Online November 2013.
- https://doi.org/10.2991/icmt-13.2013.114How to use a DOI?
- Lucas-Kanade algorithm Least trimmed squares
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lin Yih-Lon PY - 2013/11 DA - 2013/11 TI - Robust Estimation of Parameters for Lucas-Kanade Algorithm BT - 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 - https://doi.org/10.2991/icmt-13.2013.114 ID - Yih-Lon2013/11 ER -