A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Robust Real-Time Face Detection
International Journal of Computer Vision
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Pattern Recognition Letters
Tracking of vehicle trajectory by combining a camera and a laser rangefinder
Machine Vision and Applications
Fusing multiple video sensors for surveillance
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Markov Chain Monte Carlo Modular Ensemble Tracking
Image and Vision Computing
Hi-index | 0.00 |
Two basic facts motivate this paper: (1) particle filter based trackers have become increasingly powerful in recent years, and (2) object detectors using statistical learning algorithms often work at a near real-time rate. We present the use of classifiers as likelihood observation function of a particle filter. The original resulting method is able to simultaneously recognize and track an object using only a statistical model learnt from a generic database. Our main contribution is the definition of a likelihood function which is produced directly from the outputs of a classifier. This function is an estimation of calibrated probabilities P(class|data). Parameters of the function are estimated to minimize the negative log likelihood of the training data, which is a cross-entropy error function. Since a generic statistical model is used, the tracking does not need any image based model learnt inline. Moreover, the tracking is robust to appearance variation because the statistical learning is trained with many poses, illumination conditions and instances of the object. We have implemented the method for two recent popular classifiers: (1) Support Vector Machines and (2) Adaboost. An experimental evaluation shows that the approach can be used for popular applications like pedestrian or vehicle detection and tracking. Finally, we demonstrate that an efficient implementation provides a real-time system on which only a fraction of CPU time is required to track at frame rate.