Tracking and data association
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colour Model Selection and Adaption in Dynamic Scenes
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
Rao-Blackwellised particle filter for colour-based tracking
Pattern Recognition Letters
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
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Robust tracking of non-rigid objects in a dynamic environment is a challenging task. This paper presents a particle filter solution for non-stationary color tracking using a transductive local exploration algorithm. The target model is represented by a non-parametric density estimation and the similarity measure is based on a metric derived from mutual information. We employ a transductive inference to update the target model dynamically. Combining confidently labeled data and weighted unlabeled data, the proposed transductive inference offers an effective way to transduce object color model through the given observations in non-stationary color distributions. Better proposal distributions containing new observations are obtained through a method of local exploration. Targets can be tracked well despite severe occlusions or clutter. The way the transductive adaptable object model and local exploration particle filter are combined plays a decisive role in the robustness and efficiency of the tracker. In the presented tracking examples, the new approach successfully coped with target appearance variations, severe occlusions and clutters.