This paper presents a robust algorithm for automatic tracking of feature points on the human heart. The emphases and key contributions of the proposed algorithm are uniform distribution of the feature points and sustained tolerable tracking error. While in many methods in the literature, detection takes place independently from the tracking procedure, adopting a different approach, we selected a data-driven detection stage, which works based on the feedback from tracking results from the Lucas–Kanade (LK) tracking algorithm to avoid unacceptable tracking errors. To ensure a uniform spatial distribution of the total detected feature points for tracking, a cost function is employed using the simulated annealing optimizer, which prevents the newly detected points from accumulating near the previously located points or stagnant regions. Implementing the proposed algorithm on a real human heart dataset showed that the presented algorithm yields more robust tracking and improved motion reconstruction, compared with the other available methods. Furthermore, to predict the motion of feature points for handling short-term occlusions, a state space model is utilized, and thin-plate spline (TPS) interpolation was also employed to estimate motion of any arbitrary point on the heart surface.