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2010
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13 years 2 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina
ECCV
2010
Springer
13 years 8 months ago
Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera
Tracking multiple objects is important in many application domains. We propose a novel algorithm for multi-object tracking that is capable of working under very challenging conditi...
Wongun Choi, Silvio Savarese
AAAI
2008
13 years 10 months ago
Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks
Particle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distribu...
Cédric Rose, Jamal Saboune, François...

Publication
264views
13 years 4 months ago
Combined feature evaluation for adaptive visual object tracking
Existing visual tracking methods are challenged by object and background appearance variations, which often occur in a long duration tracking. In this paper, we propose a combined ...
Zhenjun Han, Qixiang Ye, Jianbin Jiao
ICIP
2007
IEEE
14 years 9 months ago
Isomap Tracking with Particle Filtering
The problem of tracking involves challenges like in-plane and out-of-plane rotations, scaling, variations in ambient light and occlusions. In this paper we look at the problem of ...
Nikhil Rane, Stanley T. Birchfield