Probabilistic Tracking with Adaptive Feature Selection

Authors:
Hwann-Tzong Chen;Tyng-Luh Liu;Chiou-Shann Fuh
Affiliations:
Academia Sinica, Taipei, Taiwan/ National Taiwan University, Taipei, Taiwan;Academia Sinica, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Year:
2004

Citing 0
Cited 8

Robust Tracking Using Foreground-Background Texture Discrimination

International Journal of Computer Vision
Adaptive weighting of local classifiers by particle filters for robust tracking

Pattern Recognition
Online selection of tracking features using AdaBoost

IEEE Transactions on Circuits and Systems for Video Technology
An on-line interactive self-adaptive image classification framework

ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Increasing classification robustness with adaptive features

ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Assessment of the influence of adaptive components in trainable surface inspection systems

Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Background subtraction for automated multisensor surveillance: a comprehensive review

EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Visual tracking using online semi-supervised learning

ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose a color-based tracking framework that infers alternately an object's configuration and good color features via particle filtering. The tracker adaptively selects discriminative color features that well distinguish foregrounds from backgrounds. The effectiveness of a feature is weighted by the Kullback-Leibler observation model, which measures dissimilarities between the color histograms of foregrounds and backgrounds. Experimental results show that the probabilistic tracker with adaptive feature selection is resilient to lighting changes and background distractions.