IEEE Transactions on Pattern Analysis and Machine Intelligence
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Density-Based Appearance Modeling for Object Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Good learners for evil teachers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Supervised learning from multiple experts: whom to trust when everyone lies a bit
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Online Empirical Evaluation of Tracking Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative spatial attention for robust tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking by fusing multiple cues with context-sensitive reliabilities
Pattern Recognition
Learning affinities and dependencies for multi-target tracking using a CRF model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Minimum error bounded efficient $/ell _1$ tracker with occlusion detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scribble Tracker: A Matting-Based Approach for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic appearance model for particle filter based visual tracking
Pattern Recognition
Efficient online structured output learning for keypoint-based object tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Robust tracking with weighted online structured learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Notwithstanding many years of progress, visual tracking is still a difficult but important problem. Since most top-performing tracking methods have their strengths and weaknesses and are suited for handling only a certain type of variation, one of the next challenges is to integrate all these methods and address the problem of long-term persistent tracking in ever-changing environments. Towards this goal, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth are provided by multiple imperfect oracles (i.e., different trackers). These trackers naturally have intrinsic diversity due to their different design strategies, and we propose a probabilistic method to simultaneously infer the most likely object position by considering the outputs of all trackers, and estimate the accuracy of each tracker. An online evaluation strategy of trackers and a heuristic training data selection scheme are adopted to make the inference more effective and efficient. Consequently, the proposed method can avoid the pitfalls of purely single tracking methods and get reliably labeled samples to incrementally update each tracker (if it is an appearance-adaptive tracker) to capture the appearance changes. Extensive experiments on challenging video sequences demonstrate the robustness and effectiveness of the proposed method.