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» A Bayesian Framework for Multi-cue 3D Object Tracking
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TSP
2010
13 years 2 months ago
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimat...
Daniel Clark, Branko Ristic, Ba-Ngu Vo, Ba-Tuong V...
ICCV
2011
IEEE
12 years 7 months ago
Realtime Multibody Visual SLAM with a Smoothly Moving Monocular Camera
This paper presents a realtime, incremental multibody visual SLAM system that allows choosing between full 3D reconstruction or simply tracking of the moving objects. Motion recon...
Abhijit Kundu, Madhava Krishna, C. V. Jawahar
CVPR
2008
IEEE
14 years 9 months ago
Drift-free tracking of rigid and articulated objects
Model-based 3D tracker estimate the position, rotation, and joint angles of a given model from video data of one or multiple cameras. They often rely on image features that are tr...
Juergen Gall, Bodo Rosenhahn, Hans-Peter Seidel
IJCV
2010
261views more  IJCV 2010»
13 years 6 months ago
HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion
While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing meth...
Leonid Sigal, Alexandru O. Balan, Michael J. Black
TROB
2008
118views more  TROB 2008»
13 years 7 months ago
A New Kalman-Filter-Based Framework for Fast and Accurate Visual Tracking of Rigid Objects
The best of Kalman-filter-based frameworks reported in the literature for rigid object tracking work well only if the object motions are smooth (which allows for tight uncertainty ...
Youngrock Yoon, Akio Kosaka, Avinash C. Kak