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» A Bayesian Framework for Multi-cue 3D Object Tracking
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PAMI
2008
275views more  PAMI 2008»
13 years 7 months ago
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
Abstract-- We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approac...
Bastian Leibe, Konrad Schindler, Nico Cornelis, Lu...
ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
14 years 1 months ago
Variable Number of "Informative" Particles for Object Tracking
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on t...
Yu Huang, Joan Llach
ICRA
2005
IEEE
105views Robotics» more  ICRA 2005»
14 years 1 months ago
A New Approach to the Use of Edge Extremities for Model-based Object Tracking
— This paper presents a robust model-based visual tracking algorithm that can give accurate 3D pose of a rigid object. Our tracking algorithm uses an incremental pose update sche...
Youngrock Yoon, Akio Kosaka, Jae Byung Park, Avina...
ISBI
2007
IEEE
14 years 1 months ago
Advanced Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images
Quantitative analysis of dynamical processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. D...
Ihor Smal, Wiro J. Niessen, Erik H. W. Meijering
ECCV
2002
Springer
14 years 9 months ago
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
Hedvig Sidenbladh, Michael J. Black, Leonid Sigal