Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
An image-based tracking algorithm for hybrid wireless sensor networks using epipolar geometry
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Tracking people in video sequences using multiple models
Multimedia Tools and Applications
Conditional localization and mapping using stereo camera
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Multibandwidth kernel-based object tracking
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Enhancing the point feature tracker by adaptive modelling of the feature support
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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A new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence-based tracking. These conditional filters allow solving systems whose measurements and state equation are estimated from the image data. In particular, the model that is considered for point tracking combines a state equation relying on the optical flow constraint and measurements provided by a matching technique. Based on this, two point trackers are derived. The first one is a linear tracker well suited to image sequences exhibiting global-dominant motion. This filter is determined through the use of a new estimator, called the conditional linear minimum variance estimator. The second one is a nonlinear tracker, implemented from a conditional particle filter. It allows tracking of points whose motion may be only locally described. These conditional trackers significantly improve results in some general situations. In particular, they allow for dealing with noisy sequences, abrupt changes of trajectories, occlusions, and cluttered background.