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» Likelihood Map Fusion for Visual Object Tracking
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SCALESPACE
2001
Springer
13 years 9 months ago
A Multi-scale Feature Likelihood Map for Direct Evaluation of Object Hypotheses
This paper develops and investigates a new approach for evaluating feature based object hypotheses in a direct way. The idea is to compute a feature likelihood map (FLM), which is ...
Ivan Laptev, Tony Lindeberg
AMFG
2003
IEEE
148views Biometrics» more  AMFG 2003»
13 years 10 months ago
Multi-Modal Face Tracking Using Bayesian Network
This paper presents a Bayesian network based multimodal fusion method for robust and real-time face tracking. The Bayesian network integrates a prior of second order system dynami...
Fang Liu, Xueyin Lin, Stan Z. Li, Yuanchun Shi
ICCV
2001
IEEE
14 years 7 months ago
Continuous Global Evidence-Based Bayesian Modality Fusion for Simultaneous Tracking of Multiple Objects
Robust, real-time tracking of objects from visual data requires probabilistic fusion of multiple visual cues. Previous approaches have either been ad hoc or relied on a Bayesian n...
Jamie Sherrah, Shaogang Gong
VMV
2001
178views Visualization» more  VMV 2001»
13 years 6 months ago
Consistent Visual Information Processing Applied to Object Recognition Landmark Definition and Real-Time Tracking
The handling of situations where multiple visual information occurs requires the fusion of visual information. This is a very common task found in the processing of multisource / ...
Axel Pinz
ICIP
2007
IEEE
14 years 7 months ago
Multi-Modal Particle Filtering Tracking using Appearance, Motion and Audio Likelihoods
We propose a multi-modal object tracking algorithm that combines appearance, motion and audio information in a particle filter. The proposed tracker fuses at the likelihood level ...
Matteo Bregonzio, Murtaza Taj, Andrea Cavallaro