Tracking a Dynamic Set of Feature Points
MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH
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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 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.
- Target Direction, Range and Position Finding