IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Dynamic Appearance Modeling for Human Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
Visual tracking via adaptive structural local sparse appearance model
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
Hough-based tracking of non-rigid objects
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Struck: Structured output tracking with kernels
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Structured class-labels in random forests for semantic image labelling
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Structure Preserving Object Tracking
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
Part-Based Visual Tracking with Online Latent Structural Learning
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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Segmentation-based tracking methods are a class of powerful tracking methods that have been highly successful in alleviating model drift during online-learning of the trackers. These methods typically include a detection component and a segmentation component, in which the tracked objects are first located by detection; then the results from detection are used to guide the process of segmentation to reduce the noises in the training data. However, one of the limitations is that the processes of detection and segmentation are treated entirely separately. The drift from detection may affect the results of segmentation. This also aggravates the tracker's drift. In this paper, we propose a novel method to address this limitation by incorporating structured labeling information in the partial least square analysis algorithms for simultaneous object tracking and segmentation. This allows for novel structured labeling constraints to be placed directly on the tracked objects to provide useful contour constraint to alleviate the drifting problem. We show through both visual results and quantitative measurements on the challenging sequences that our method produces more robust tracking results while obtaining accurate object segmentation results.