Vehicle detection methods for surveillance applications

O. Sidla; E. Wildling; Y. Lypetskyy

doi:10.1117/12.683442

2 October 2006 Vehicle detection methods for surveillance applications

O. Sidla, E. Wildling, Y. Lypetskyy

Proceedings Volume 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision; 63840D (2006) https://doi.org/10.1117/12.683442
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

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.

Citation Download Citation

O. Sidla, E. Wildling, and Y. Lypetskyy "Vehicle detection methods for surveillance applications", Proc. SPIE 6384, Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision, 63840D (2 October 2006); https://doi.org/10.1117/12.683442

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