Active vision
A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Bayesian Object Localisation in Images
International Journal of Computer Vision
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Video object tracking using adaptive Kalman filter
Journal of Visual Communication and Image Representation
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Spatio-temporal graphical-model-based multiple facial feature tracking
EURASIP Journal on Applied Signal Processing
Particle Filter Based Object Tracking with Discriminative Feature Extraction and Fusion
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Robust human tracking based on multi-cue integration and mean-shift
Pattern Recognition Letters
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Fast face tracking using parallel particle filter algorithm
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Parallel particle filter algorithm in face tracking
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multi-object tracking based on a modular knowledge hierarchy
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
A parallel histogram-based particle filter for object tracking on SIMD-based smart cameras
Computer Vision and Image Understanding
Rao-Blackwellised particle filter for colour-based tracking
Pattern Recognition Letters
Particle filtering with factorized likelihoods for tracking facial features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
International Journal of Computational Vision and Robotics
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
Robust tracking with and beyond visible spectrum: a four-layer data fusion framework
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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An important issue in tracking is how to incorporate an appropriate degree of adaptivity into the observation model. Without any adaptivity, tracking fails when object properties change, for example when illumination changes affect surface colour. Conversely, if an observation model adapts too readily then, during some transient failure of tracking, it is liable to adapt erroneously to some part of the background. The approach proposed here is to adapt selectively, allowing adaptation only during periods when two particular conditions are met: that the object should be both present and in motion. The proposed mechanism for adaptivity is tested here with a foreground colour and motion model. The experimental setting itself is novel in that it uses combined colour and motion observations from a fixed filter bank, with motion used also for initialisation via a Monte Carlo proposal distribution. Adaptation is performed using a stochastic EM algorithm, during periods that meet the conditions above. Tests verify the value of such adaptivity, in that immunity to distraction from clutter of similar colour to the object is considerably enhanced.