Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering

Fernando, W., Udawatta, Lanka and Pathirana, Pubudu 2007, Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering, in ICIAfS 2007 the 3rd International Conference on Information and Automation for Sustainability, The Institute of Electrical and Electronics Engineers, Inc (IEEE), Piscataway, N.J., pp. 111-117.

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Title Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering
Author(s) Fernando, W.
Udawatta, Lanka
Pathirana, Pubudu
Conference name International Conference on Information and Automation for Sustainability (3rd: 2007: Melbourne, Vic.)
Conference location Melbourne, Australia
Conference dates 4-6 December 2007
Title of proceedings ICIAfS 2007 the 3rd International Conference on Information and Automation for Sustainability
Editor(s) [Unknown]
Publication date 2007
Conference series International Conference on Information and Automation for Sustainability
Start page 111
End page 117
Publisher The Institute of Electrical and Electronics Engineers, Inc (IEEE)
Place of publication Piscataway, N.J.
Keyword(s) optical flow
feature point
k means clustering
centroid
Summary This paper describes the methodology for identifying moving obstacles by obtaining a reliable and a sparse optical flow from image sequences. Given a sequence of images, basically we can detect two-types of on road vehicles, vehicles traveling in the opposite direction and vehicles traveling in the same direction. For both types, distinct feature points can be detected by Shi and Tomasi corner detector algorithm. Then pyramidal Lucas Kanade method for optical flow calculation is used to match the sparse feature set of one frame on the consecutive frame. By applying k means clustering on four component feature vector, which are to be the coordinates of the feature point and the two components of the optical flow, we can easily calculate the centroids of the clusters and the objects can be easily tracked. The vehicles traveling in the opposite direction produce a diverging vector field, while vehicles traveling in the same direction produce a converging vector field
ISBN 9781424419005
Language eng
Field of Research 090904 Navigation and Position Fixing
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008092

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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