Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
ACM Computing Surveys (CSUR)
Incremental Learning for Robust Visual Tracking
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
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
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
Context tracker: Exploring supporters and distracters in unconstrained environments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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This paper proposes an online tracking method which has been inspired by studying the effects of Scale Invariant Feature Transform (SIFT) when applied to objects assumed to be flat even though they are not. The consequent deviation from flatness induces nuisance factors that act on the feature representation in a manner for which no general local invariants can be computed, such as in the case of occlusion, sensor quantization and casting shadows. However, if features are over-represented, they can provide the necessary information to build online, a robust object/context discriminative classifier. This is achieved based on weakly aligned multiple instance local features in a sense that will be made clear in the rest of this paper. According to this observation, we present a non parametric online tracking by detection approach that yields state of the art performance. Specific tests on video sequences of faces show excellent long-term tracking performance in unconstrained videos.