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Proceedings Paper

Red-light traffic enforcement at railway crossings
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Paper Abstract

The observation and monitoring of traffic witih smart vision systems for the purpose of improving traffic safety has a big potential. Embedded loop sensors can detect and count passing vehicles, radar can measure speed and presence of vehicles, and embedded vision systems or stationary camera systems can count vehicles and estimate the state of traffic along the road. This work presents a vision system which is targeted at detecting and reporting incidents at unsecured railways crossings. These crossings, even when guarded by automated barriers, pose a threat to drivers day and night. Our system is designed to detect and record vehicles which pass over the railway crossing by means of real-time motion analysis after the red light has been activated. We implement sparse optical flow in conjunction with motion clustering in order to detect critical events. We describe some modifications of the original Lucas Kanade optical flow method which makes our implementation faster and more robust compared to the original concept. In addition, the results of our optical flow method are compared with a HOG based vehicle detector which has been implemented and tested as an alternative methodology. The embedded system which is used for detection consists of a smart camera which observes one street lane as* well as the red light at the crossing. The camera is triggered by an electrical signal from the railway as soon ss a vehicle moves over th this line, image sequences are recorded and stored onboard the device.

Paper Details

Date Published: 23 January 2012
PDF: 12 pages
Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 83010B (23 January 2012); doi: 10.1117/12.908369
Show Author Affiliations
Oliver Sidla, SLR Engineering OG (Austria)
Gernot Loibner, SLR Engineering OG (Austria)


Published in SPIE Proceedings Vol. 8301:
Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques
Juha Röning; David P. Casasent, Editor(s)

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