The obstacle detection on the railway crossing based on optical flow and clustering

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...

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Bibliographische Detailangaben
Hauptverfasser:	Silar, Zdenek, Dobrovolny, Martin
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Adaptive optics Background Estimation Clustering algorithms Computer vision Estimation Image motion analysis K-means Clustering Matlab Objects Detection Optical Flow Optical imaging Railway Crossing Monitoring Vectors Velocity Vectors
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Beschreibung
Zusammenfassung:	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.
DOI:	10.1109/TSP.2013.6614039