Deakin University
Browse

File(s) under permanent embargo

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 Pathirana
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

History

Event

International Conference on Information and Automation for Sustainability (3rd: 2007: Melbourne, Vic.)

Pagination

111 - 117

Publisher

The Institute of Electrical and Electronics Engineers, Inc (IEEE)

Location

Melbourne, Australia

Place of publication

Piscataway, N.J.

Start date

2007-12-04

End date

2007-12-06

ISBN-13

9781424419005

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2007, IEEE

Title of proceedings

ICIAfS 2007 the 3rd International Conference on Information and Automation for Sustainability

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC