Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Point Correspondence in the Presence of Occlusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Establishing motion correspondence
CVGIP: Image Understanding
Visual tracking of known three-dimensional objects
International Journal of Computer Vision
An introduction to genetic algorithms
An introduction to genetic algorithms
Human motion estimation from monocular image sequence based on cross-entropy regularization
Pattern Recognition Letters
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Articulated motion reconstruction from feature points
Pattern Recognition
A Trajectory-Based Point Tracker Using Chaos Evolutionary Programming
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
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This paper proposes a genetic algorithm-based approach to feature point correspondence between consecutive frames in a long image sequence containing outliers and occlusions. α-β-γ filter-based visual tracking is proposed to track the motion of feature points between the consecutive image frames according to the characteristics of high speed of image sampling and motion inertia. Then, the chromosome and the fitness function of the genetic algorithm are constructed based on factors such as tracking results of feature point, occlusion problem, outliers, and motion smoothness in image frames. Finally, the correspondence is obtained by using the genetic algorithm. Experimental results from a real image sequence demonstrate the effectiveness of the proposed approach for removing outliers and recovering occluded points.