EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Tracking and Vehicle Classification via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification from Random Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time visual tracking using compressive sensing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Decoding by linear programming
IEEE Transactions on Information Theory
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Object tracking is a challenging problem in computer vision community. It is very difficult to solve it efficiently due to the appearance or motion changes of the object, such as pose, occlusion, or illumination. Existing online tracking algorithms often update models with samples from observations in recent frames. And some successful tracking algorithms use more complex models to make the performance better. But most of them take a long time to detect the object. In this paper, we proposed an effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis and a motion mode based on Gaussian perturbation. In addition, the features used in our approach are compressed in a small vector, making the classifier more efficient. The motion model based on random Gaussian distribution makes the performance more effective. The proposed algorithm runs in real-time and performs very well against some existing algorithms on challenging sequences.