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» Learning to Track with Multiple Observers
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ICPR
2006
IEEE
14 years 1 months ago
Part Based Human Tracking In A Multiple Cues Fusion Framework
This paper presents a real time video surveillance system which is capable of tracking multiple humans simultaneously. To better deal with various challenging issues such as occlu...
Qi Zhao, Jinman Kang, Hai Tao, Wei Hua
AAAI
2012
11 years 10 months ago
Competing with Humans at Fantasy Football: Team Formation in Large Partially-Observable Domains
We present the first real-world benchmark for sequentiallyoptimal team formation, working within the framework of a class of online football prediction games known as Fantasy Foo...
Tim Matthews, Sarvapali D. Ramchurn, Georgios Chal...
ICIP
2007
IEEE
14 years 2 months ago
Energetic Particle Filter for Online Multiple Target Tracking
Online target tracking requires to solve two problems: data association and online dynamic estimation. Usually, association effectiveness is based on prior information and observa...
Abir El Abed, Séverine Dubuisson, Dominique...
CVPR
2010
IEEE
14 years 3 months ago
Online Multiple Instance Learning with No Regret
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been u...
Li Mu, James Kwok, Lu Bao-liang
ICRA
2009
IEEE
179views Robotics» more  ICRA 2009»
14 years 2 months ago
Automatic weight learning for multiple data sources when learning from demonstration
— Traditional approaches to programming robots are generally inaccessible to non-robotics-experts. A promising exception is the Learning from Demonstration paradigm. Here a polic...
Brenna Argall, Brett Browning, Manuela M. Veloso