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
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Robust Real-Time Face Detection
International Journal of Computer Vision
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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
Adaptive Weighting of Local Classifiers by Particle Filter
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Fast, Accurate and Robust Recognition Based On Local Normalized Linear Summation Kernel
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Active appearance models with occlusion
Image and Vision Computing
Feature harvesting for tracking-by-detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Probabilistic multiple face detection and tracking using entropy measures
IEEE Transactions on Circuits and Systems for Video Technology
Detection and tracking faces in unconstrained color video streams
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Correlation-based incremental visual tracking
Pattern Recognition
A compact association of particle filtering and kernel based object tracking
Pattern Recognition
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This paper presents an adaptive weighting method for combining local classifiers using a particle filter. Although the effectiveness of weighting methods based on combinations of local classifiers (features) has been reported recently, such methods fail in cases where there is partial occlusion or when shadows appear due to changes in the illumination direction since fixed weights are used for combining the local classifiers. In order to achieve the desired robustness, the weights should be changed adaptively. For this purpose, we use a particle filter, where each particle is assigned to the weight set for combining local classifiers. By estimating the posterior probability in weight space by using a particle filter, the effective weights for current time-step are obtained, and as a result the proposed method can account for dynamic occlusion. As a means of a demonstration, our approach is applied to the face tracking problem. The adaptability and the robustness of the method with respect to partial occlusion are evaluated using test sequences in which the occluded areas are changed dynamically. The weights of the occluded regions decrease automatically without the need for explicit knowledge about the occurrence of occlusion, which makes it possible to track the face under conditions of dynamic occlusion.