DocumentCode
3252572
Title
The obstacle detection on the railway crossing based on optical flow and clustering
Author
Silar, Zdenek ; Dobrovolny, M.
fYear
2013
fDate
2-4 July 2013
Firstpage
755
Lastpage
759
Abstract
This article deals with the obstacle detection on a railway crossing (clearance detection). The presented detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. The optical flow is based on a modified Lucas-Kanade method. For testing of the developed methods a model was created and the results were verified on a real data.
Keywords
image classification; image sequences; object detection; pattern clustering; K-means clustering algorithm; clearance detection; flow vector classification; modified Lucas-Kanade method; obstacle detection; optical flow estimation; railway crossing; Adaptive optics; Clustering algorithms; Computer vision; Estimation; Image motion analysis; Optical imaging; Vectors; Background Estimation; K-means Clustering; Matlab; Objects Detection; Optical Flow; Railway Crossing Monitoring; Velocity Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Telecommunications and Signal Processing (TSP), 2013 36th International Conference on
Conference_Location
Rome
Print_ISBN
978-1-4799-0402-0
Type
conf
DOI
10.1109/TSP.2013.6614039
Filename
6614039
Link To Document