Human Tracking Using Sequential Importance Sampling Particle Filter by Omnidirectional Camera
Date Issued
2007
Date
2007
Author(s)
Song, Chao-Jung
DOI
en-US
Abstract
Visual tracking is an important topic in computer vision and robotics fields. The omnidirectional cameras provide a wider filed of view, but tracking with an omnidirectional camera must overcome the warping and low resolution drawback. This thesis presents an approach based on the sequential importance sampling particle filter framework to track multiple humans using an omnidirectional camera. In order to efficiently converge to the target distribution, a foreground-based importance sampling mechanism using foreground segmentation algorithm is proposed to draw particles from currently observed image. The fusion of color and contour features to evaluate the likelihood measurement makes human tracking more accurate. Likelihood evaluation by integrating two-space enhances the robustness of the system to the warping effect. The overall performance is validated using several videos in the experiments.
Subjects
影像追蹤
多目標物追蹤
全方位攝影機
粒子濾波器
Visual Tracking
Human Tracking
Omnidirectional Camera
Particle Filter
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-96-R94921009-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):3e3ab6a0a2c1071dbf5400dd7986cf47