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» Likelihood Map Fusion for Visual Object Tracking
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WACV
2008
IEEE
13 years 11 months ago
Likelihood Map Fusion for Visual Object Tracking
Visual object tracking can be considered as a figure-ground classification task. In this paper, different features are used to generate a set of likelihood maps for each pixel i...
Zhaozheng Yin, Fatih Porikli, Robert T. Collins
AMDO
2004
Springer
13 years 10 months ago
Image Cues Fusion for Object Tracking Based on Particle Filter
Particle filter is a powerful algorithm to deal with non-linear and non-Gaussian tracking problems. However the algorithm relying only upon one image cue often fails in challengin...
Peihua Li, François Chaumette
ICIP
2007
IEEE
14 years 6 months ago
Hierarchical Feature Fusion for Visual Tracking
A new method for object tracking in video sequences is presented. This method exploits the benefits of particle filters to tackle the multimodal distributions emerging from clutte...
Alexandros Makris, Dimitrios I. Kosmopoulos, Stavr...
ISVC
2005
Springer
13 years 10 months ago
Distributed Multi-camera Surveillance for Aircraft Servicing Operations
This paper presents the visual surveillance aspects of a distributed intelligent system that has been developed in the context of aircraft activity monitoring. The overall trackin...
David Thirde, Mark Borg, James M. Ferryman, Josep ...
ICCV
2007
IEEE
13 years 6 months ago
Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering
Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multip...
Bohyung Han, Seong-Wook Joo, Larry S. Davis