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*.*This paper presents a model-based algorithm for tracking feature points over a long sequence of monocular noisy images with the ability to include new feature points detected in successive frames. The trajectory for each feature point is modeled by a simple kinematic motion model. A Probabilistic Data Association Filter is first designed to estimate the motion between two consecutive frames. A matching algorithm then identifies the corresponding point to subpixel accuracy and P an Extended Kalman Filter (EKF) is employed to continually track the feature point. An efficient way to dynamically include new feature points from successive frames into a tracking list is also addressed. Tracking results for several image sequences are given.
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