The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Noise Model of Support Vector Machine Regression
On the Noise Model of Support Vector Machine Regression
A tutorial on support vector regression
Statistics and Computing
A finite-horizon adaptive Kalman filter for linear systems with unknown disturbances
Signal Processing - Signal processing in communications
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robust Pose Estimation from a Planar Target
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video object tracking using adaptive Kalman filter
Journal of Visual Communication and Image Representation
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Bayesian support vector regression using a unified loss function
IEEE Transactions on Neural Networks
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Recursive Bayesian Estimation (RBE) is a widespread solution for visual tracking as well as for applications in other domains where a hidden state is estimated recursively from noisy measurements. From a practical point of view, deployment of RBE filters is limited by the assumption of complete knowledge on the process and measurement statistics. These missing tokens of information lead to an approximate or even uninformed assignment of filter parameters. Unfortunately, the use of the wrong transition or measurement model may lead to large estimation errors or to divergence, even when the otherwise optimal filter is deployed. In this paper on-line learning of the transition model via Support Vector Regression is proposed. The specialization of this general framework for linear/Gaussian filters, which we dub Support Vector Kalman (SVK), is then introduced and shown to outperform a standard, non adaptive Kalman filter as well as a widespread solution to cope with unknown transition models such as the Interacting Multiple Models (IMM) filter.