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» Tracking in Reinforcement Learning
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AVSS
2006
IEEE
15 years 4 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
149
Voted
WACV
2012
IEEE
13 years 8 months ago
Online discriminative object tracking with local sparse representation
We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with a...
Qing Wang, Feng Chen, Wenli Xu, Ming-Hsuan Yang
157
Voted
IBPRIA
2007
Springer
15 years 2 months ago
Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation
This work describes an approach for the interpretation and explanation of human behavior in image sequences, within the context of a Cognitive Vision System. The information source...
Pau Baiget, Carles Fernández Tena, F. Xavie...
ECCV
2008
Springer
16 years 2 months ago
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
Visual tracking is a challenging problem, as an object may change its appearance due to viewpoint variations, illumination changes, and occlusion. Also, an object may leave the fie...
Gérard G. Medioni, Qian Yu, Thang Ba Dinh
ECCV
2004
Springer
16 years 2 months ago
Tracking Aspects of the Foreground against the Background
In object tracking, change of object aspect is a cause of failure due to significant changes of object appearances. The paper proposes an approach to this problem without a priori ...
Hieu Tat Nguyen, Arnold W. M. Smeulders