Machine Learning
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Machine Learning
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
POP: Patchwork of Parts Models for Object Recognition
International Journal of Computer Vision
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-loop adaptation for robust tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Backprojection revisited: scalable multi-view object detection and similarity metrics for detections
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
On-Line Random Naive Bayes for Tracking
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Interactive multi-label segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
An adaptive coupled-layer visual model for robust visual tracking
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
Object tracking using learned feature manifolds
Computer Vision and Image Understanding
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Online learning has shown to be successful in tracking-by-detection of previously unknown objects. However, most approaches are limited to a bounding-box representation with fixed aspect ratio and cannot handle highly non-rigid and articulated objects. Moreover, they provide only a limited foreground/background separation, which in turn, increases the amount of noise introduced during online self-training. To overcome the limitations of a rigid bounding box, we present a novel tracking-by-detection approach based on the generalized Hough-transform. We extend the idea of Hough Forests to the online domain and couple the voting-based detection and back-projection with a rough GrabCut segmentation. Because of the increased granularity of the object description the amount of noisy training samples during online learning is reduced significantly which prevents drifting of the tracker. To show the benefits of our approach, we demonstrate it for a variety of previously unknown objects even under heavy non-rigid transformations, partial occlusions, scale changes, and rotations. Moreover, we compare our tracker to state-of-the-art methods (bounding-box-based as well as part-based) and show robust and accurate tracking results on various challenging sequences.