Least Trimmed Squares Approach to Lucas-Kanade Algorithm in Object Tracking ProblemsReport as inadecuate

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Mathematical Problems in EngineeringVolume 2013 2013, Article ID 324824, 6 pages

Research ArticleDepartment of Information Engineering, I-Shou University, Kaohsiung 84001, Taiwan

Received 11 April 2013; Accepted 19 June 2013

Academic Editor: Ker-Wei Yu

Copyright © 2013 Yih-Lon Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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. Although this method is time consuming, it is effective in tracking accuracy and environment adaptation. In the standard LK method, the 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 and occlusions in the tracking process. Simulations are 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 problems.

Author: Yih-Lon Lin

Source: https://www.hindawi.com/


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