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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
Author(s)
Lim, Young ChulKang, Minsung
Issued Date
2014-09
Citation
11th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2014, pp.788 - 793
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
DOI
10.5220/0005147807880793
Publisher
INSTICC – Institute for Systems and Technologies of Information, Control and Communication
Related Researcher
  • 임영철 Lim, Young Chul
  • Research Interests Deep learning;딥러닝; object detection;객체검출; re-identification;재식별; multi-object tracking;다중객체추적; multi-camera video analysis;다중카메라영상분석
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Division of Automotive Technology 2. Conference Papers

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