Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association
Feature Point Correspondence in the Presence of Occlusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Establishing motion correspondence
CVGIP: Image Understanding
A review of statistical data association for motion correspondence
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Object Classification for Automated Surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
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This paper studies the point correspondence problem for which a diversity of qualitative and statistical solutions exist. Most of them use local optimizations between neighboring frames to determine trajectories for moving points. We present improved extensive algorithm using dynamic programming method which provides global optimum for functional based both on nearest neighbor and smooth motion models. We considered dynamic scenes with multiple, independently moving objects in which feature points may enter and leave the view field. Experiments with real and synthetic data are presented to validate the claims about the performance of the proposed algorithm.