CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th 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
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
Dynamic Integration of Generalized Cues for Person Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Tracking by parts: a Bayesian approach with component collaboration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive sensor fault detection and identification using particle filter algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Integration of Fuzzy Spatial Information in Tracking Based on Particle Filtering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
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Many researchers argue that fusing multiple cues increases the reliability and robustness of visual tracking. However, how the multi-cue integration is realized during tracking is still an open issue. In this work, we present a novel data fusion approach for multi-cue tracking using particle filter. Our method differs from previous approaches in a number of ways. First, we carry out the integration of cues both in making predictions about the target object and in verifying them through observations. Our second and more significant contribution is that both stages of integration directly depend on the dynamically changing reliabilities of visual cues. These two aspects of our method allow the tracker to easily adapt itself to the changes in the context, and accordingly improve the tracking accuracy by resolving the ambiguities.