Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
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
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Robust tracking using local sparse appearance model and K-selection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Context tracker: Exploring supporters and distracters in unconstrained environments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Visual tracking is a challenging problem, because the target frequently change its appearance, randomly move its location and get occluded by other objects in unconstrained environments. The state changes of the target are temporally and spatially continuous, in this paper therefore, a robust Spatio-Temporal structural context based Tracker (STT) is presented to complete the tracking task in unconstrained environments. The temporal context capture the historical appearance information of the target to prevent the tracker from drifting to the background in a long term tracking. The spatial context model integrates contributors, which are the key-points automatically discovered around the target, to build a supporting field. The supporting field provides much more information than appearance of the target itself so that the location of the target will be predicted more precisely. Extensive experiments on various challenging databases demonstrate the superiority of our proposed tracker over other state-of-the-art trackers.