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» Tracking in Reinforcement Learning
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TSP
2011
197views more  TSP 2011»
14 years 4 months ago
Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods
—This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems fr...
Amadou Gning, Lyudmila Mihaylova, Simon Maskell, S...
CVPR
2006
IEEE
15 years 11 months ago
BoostMotion: Boosting a Discriminative Similarity Function for Motion Estimation
Motion estimation for applications where appearance undergoes complex changes is challenging due to lack of an appropriate similarity function. In this paper, we propose to learn ...
Shaohua Kevin Zhou, Bogdan Georgescu, Dorin Comani...
ICCV
2009
IEEE
16 years 2 months ago
Correlated Probabilistic Trajectories for Pedestrian Motion Detection
This paper introduces an algorithm for detecting walking motion using point trajectories in video sequences. Given a number of point trajectories, we identify those which are sp...
Frank Perbet, Atsuto Maki, Bjorn Stenger
69
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ICPR
2004
IEEE
15 years 10 months ago
Multi-Resolution Template Kernels
Domains in which shapes of objects change rapidly and significantly are a challenge for existing representation techniques: sport is a good example of this. We present a texture-b...
Chris J. Needham, Roger D. Boyle
ICPR
2000
IEEE
15 years 10 months ago
GlobeAll: Panoramic Video for an Intelligent Room
Choosing the appropriate type of video input is an important issue for any vision-based system and the right decision must take into account the specific requirements of the inten...
Gérard G. Medioni, Mircea Nicolescu