Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted and Robust Incremental Method for Subspace Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Simultaneous Modeling and Tracking (SMAT) of Feature Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Tracking by parts: a Bayesian approach with component collaboration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
An incremental nonlinear dimensionality reduction algorithm based on ISOMAP
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Feature harvesting for tracking-by-detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Incremental locally linear embedding algorithm
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Probabilistic Object Tracking With Dynamic Attributed Relational Feature Graph
IEEE Transactions on Circuits and Systems for Video Technology
Visual Object Tracking Based on Combination of Local Description and Global Representation
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive Object Tracking by Learning Hybrid Template Online
IEEE Transactions on Circuits and Systems for Video Technology
Video Tracking Based on Sequential Particle Filtering on Graphs
IEEE Transactions on Image Processing
Real time robust L1 tracker using accelerated proximal gradient approach
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough-based tracking of non-rigid objects
Computer Vision and Image Understanding
Co-trained generative and discriminative trackers with cascade particle filter
Computer Vision and Image Understanding
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Local feature based object tracking approaches have been promising in solving the tracking problems such as occlusions and illumination variations. However, existing approaches typically model feature variations using prototypes, and this discrete representation cannot capture the gradual changing property of local appearance. In this paper, we propose to model each local feature as a feature manifold to characterize the smooth changing behavior of the feature descriptor. The manifold is constructed from a series of transformed images simulating possible variations of the feature being tracked. We propose to build a collection of linear subspaces which approximate the original manifold as a low dimensional representation. This representation is used for object tracking. Object location is located by a feature-to-manifold matching process. Our tracking method can update the manifold status, add new feature manifolds and remove expiring ones adaptively according to object appearance. We show both qualitatively and quantitatively this representation significantly improves the tracking performance under occlusions and appearance variations using standard tracking dataset.