Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Geometric invariance in computer vision
Geometric invariance in computer vision
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Frame Correspondence Estimation Using Subspace Constraints
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A 3D Shape Constraint on Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching actions in presence of camera motion
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A globally optimal approach for 3D elastic motion estimation from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Efficient non-consecutive feature tracking for structure-from-motion
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
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Despite many alternatives to feature tracking problem, iterative least squares solution solving the optical flow constraint has been the most popular approach used by many in the field. This paper attempts to leverage the former efforts to enhance feature tracking methods by introducing a view geometric constraint to the tracking problem. In contrast to alternative geometry based methods, the proposed approach provides a closed form solution to optical flow estimation from image appearance and view geometry constraints. We particularly use invariants in the projective coordinates generated from tracked features that results in a new optical flow equation. This treatment provides persistent tracking of features even when they are occluded. At the end of each tracking loop the quality of the tracked features is judged using both appearance similarity and geometric consistency. Our experiments demonstrate robust tracking performance even when the features are occluded or they undergo appearance changes due to projective deformation of the template.