Author(s)Young Min Kim
Contributor(s)The Pennsylvania State University CiteSeerX Archives
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AbstractObject tracking has been a hot topic in the area of computer vision. A lot of research has been undergoing ranging from applications to noble algorithms. However, most works are focused on a specific application, such as tracking human, car, or pre-learned objects. In this project, objects randomly chosen by a user are tracked using SIFT features and a Kalman filter. After sufficient information about the objects are accumulated, we can exploit the learning to successfully track objects even when the objects come into the view after it had been disappeared for a few frames. Key Words- Object tracking, SIFT, Kalman filter 1.