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Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering
conference contribution
posted on 2007-01-01, 00:00 authored by W Fernando, L Udawatta, Pubudu PathiranaPubudu PathiranaThis 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
History
Event
International Conference on Information and Automation for Sustainability (3rd: 2007: Melbourne, Vic.)Pagination
111 - 117Publisher
The Institute of Electrical and Electronics Engineers, Inc (IEEE)Location
Melbourne, AustraliaPlace of publication
Piscataway, N.J.Start date
2007-12-04End date
2007-12-06ISBN-13
9781424419005Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2007, IEEETitle of proceedings
ICIAfS 2007 the 3rd International Conference on Information and Automation for SustainabilityUsage metrics
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