Robot vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Sparse Probabilistic Learning Algorithm for Real-Time Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Bayesian Filtering for Object Tracking
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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
BoostMotion: Boosting a Discriminative Similarity Function for Motion Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An unsupervised, online learning framework for moving object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Object Tracking Using Grayscale Appearance Models and Swarm Based Particle Filter
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
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This paper presents a learning-based algorithm for object tracking. During on-line learning we employ most informative and hard to classify examples, features maximizing individually the mutual information, stable object features within all past observations and features from the initial object template. The object undergoing tracking is discriminated criminated by a boosted classifier built on regression stumps. We seek mode in the confidence map calculated by the strong classifier to sample new features. In a supplementing tracker based upon a particle filter we use a recursively updated mixture appearance model, which depicts stable structures in images seen so far, initial object appearance as well as two-frame variations. The update of slowly varying component is done using only pixels that are classified by the strong classifier as belonging to foreground. The estimates calculated by particle filter allow us to sample supplementary features for learning of the classifier. The performance of the algorithm is demonstrated on freely available test sequences. The resulting algorithm runs in real-time.