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Stereo vision-based visual tracking using 3D feature clustering for robust vehicle tracking

Title
Stereo vision-based visual tracking using 3D feature clustering for robust vehicle tracking
Authors
Lim, Young-ChulKang, Minsung
DGIST Authors
Lim, Young-Chul; Kang, Minsung
Issue Date
2014
Citation
11th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2014, 2, 788-793
Type
Conference
Article Type
Conference Paper
ISBN
9789897580406
Abstract
In order to detect vehicles on the road reliably, a vehicle detector and tracker should be integrated to work in unison. In real applications, some of the ROIs generated from a vehicle detector are often ill-fitting due to imperfect detector outputs. The ill-fitting ROIs make it difficult for tracker to estimate a target vehicle correctly due to outliers. In this paper, we propose a stereo-based visual tracking method using a 3D feature clustering scheme to overcome this problem. Our method selects reliable features using feature matching and a 3D feature clustering method and estimates an accurate transform model using a modified RANSAC algorithm. Our experimental results demonstrate that the proposed method offers better performance compared with previous feature-based tracking methods.
URI
http://hdl.handle.net/20.500.11750/3795
Publisher
SciTePress
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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