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Proceedings Paper

Vehicle detection methods for surveillance applications
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Paper Abstract

The efficient monitoring of traffic flow as well as related surveillance and detection applications demand an increasingly robust recognition of vehicles in image and video data. This paper describes two different methods for vehicle detection in real world situations: Principal Component Analysis and the Histogram of Gradients principle. Both methods are described and their detection capabilities as well as advantages and disadvantages are compared. A large sample dataset which contains images of cars from the backside and frontside in day and night conditions is the basis for creating and optimizing both variants of the proposed algorithms. The resulting two detectors allow recognition of vehicles in frontal view +- 30 deg and views from behind +- 30 deg. The paper demonstrates that both detection methods can operate effectively even under difficult lighting situations with high detection rates and a low number of false positives.

Paper Details

Date Published: 2 October 2006
PDF: 8 pages
Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 63840D (2 October 2006); doi: 10.1117/12.683442
Show Author Affiliations
O. Sidla, Joanneum Research (Austria)
E. Wildling, Joanneum Research (Austria)
Y. Lypetskyy, Joanneum Research (Austria)

Published in SPIE Proceedings Vol. 6384:
Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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