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» Probabilistic Object Tracking Using Multiple Features
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ICIP
2006
IEEE
14 years 9 months ago
Recognition of Multi-Object Events Using Attribute Grammars
* We present a method for representing and recognizing visual events using attribute grammars. In contrast to conventional grammars, attribute grammars are capable of describing fe...
Seong-Wook Joo, Rama Chellappa
ICCV
2009
IEEE
13 years 5 months ago
Robust facial feature tracking using selected multi-resolution linear predictors
This paper proposes a learnt data-driven approach for accurate, real-time tracking of facial features using only intensity information. Constraints such as a-priori shape models o...
Eng-Jon Ong, Yuxuan Lan, Barry Theobald, Richard H...
CRV
2005
IEEE
136views Robotics» more  CRV 2005»
14 years 1 months ago
Coordination of Multiple Agents for Probabilistic Object Tracking
In this paper, we develop a new tracking approach which is based on cooperation and coordination of multiple agents which are pan-tilt-zoom cameras to optimize the cost of trackin...
Roozbeh Mottaghi, Shahram Payandeh
ICCV
2007
IEEE
13 years 9 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
ICIAR
2005
Springer
14 years 1 months ago
A Novel Tracking Framework Using Kalman Filtering and Elastic Matching
A novel region-based multiple object tracking framework based on Kalman filtering and elastic matching is proposed. The proposed Kalman filtering-elastic matching model is genera...
Xingzhi Luo, Suchendra M. Bhandarkar