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» Tracking in Reinforcement Learning
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CVPR
2010
IEEE
14 years 4 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu
CVPR
2012
IEEE
13 years 9 months ago
Tracking many vehicles in wide area aerial surveillance
Wide area aerial surveillance data has recently proliferated and increased the demand for multi-object tracking algorithms. However, the limited appearance information on every ta...
Jan Prokaj, Xuemei Zhao, Gérard G. Medioni
CVPR
2007
IEEE
16 years 9 months ago
Closed-Loop Tracking and Change Detection in Multi-Activity Sequences
We present a novel framework for tracking of a long sequence of human activities, including the time instances of change from one activity to the next, using a closed-loop, non-li...
Bi Song, Namrata Vaswani, Amit K. Roy Chowdhury
ICPR
2004
IEEE
16 years 8 months ago
Switching Particle Filters for Efficient Real-time Visual Tracking
Particle filtering is an approach to Bayesian estimation of intractable posterior distributions from time series signals distributed by non-Gaussian noise. A couple of variant par...
Kenji Doya, Shin Ishii, Takashi Bando, Tomohiro Sh...
CRV
2008
IEEE
295views Robotics» more  CRV 2008»
16 years 1 months ago
3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis
We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image featu...
Kooksang Moon, Vladimir Pavlovic