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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Tracking with Adaptive Feature Selection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Divide and Conquer with Motion Cues for Tracking through Clutter
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Approach to Multiple Target Detection and Tracking
IEEE Transactions on Signal Processing
A Nonparametric Adaptive Tracking Algorithm Based on Multiple Feature Distributions
IEEE Transactions on Multimedia
Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance
IEEE Transactions on Image Processing
A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior
IEEE Transactions on Image Processing
Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking
IEEE Transactions on Image Processing
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This paper presents an online feature selection algorithm for video object tracking. Using the object and background pixels from the previous frame as training samples, we model the feature selection problem as finding a good subset of features to better classify object from background in current frame. This paper aims to improve existing methods by taking correlation between features into consideration. We propose to use AdaBoost algorithm to iteratively select one feature which best compensates the previously selected features. Using the selected features, we then construct a compound likelihood image, which shows the ability to discriminate better than the original frame, as the input for the tracking process. We also propose to use ellipse fitting to eliminate mislabeled pixels from our training process. In addition, we propose an online feature validity test to monitor the selected features and only re-select features when the previously selected features become out-of-date. Experimental results demonstrate that the proposed algorithm combined with mean-shift based tracking algorithm achieves very promising results.