Sciweavers

Share
ECCV
2000
Springer

Quasi-Random Sampling for Condensation

12 years 3 months ago
Quasi-Random Sampling for Condensation
The problem of tracking pedestrians from a moving car is a challenging one. The Condensation tracking algorithm is appealing for its generality and potential for real-time implementation. However, the conventional Condensation tracker is known to have diculty with high-dimensional state spaces and unknown motion models. This paper presents an improved algorithm that addresses these problems by using a simplified motion model, and employing quasi-Monte Carlo techniques to eciently sample the resulting tracking problem in the high-dimensional state space. For N sample points, these techniques achieve sampling errors of O(N31 ), as opposed to O(N31/2 ) for conventional Monte Carlo techniques. We illustrate the algorithm by tracking objects in both synthetic and real sequences, and show that it achieves reliable tracking and significant speed-ups over conventional Monte Carlo techniques.
Vasanth Philomin, Ramani Duraiswami, Larry S. Davi
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2000
Where ECCV
Authors Vasanth Philomin, Ramani Duraiswami, Larry S. Davis
Comments (0)
books