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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Vehicle detection combining gradient analysis and AdaBoost classification
This paper presents a real-time vision-based vehicle's rear detection system using gradient based methods and Adaboost classification, for ACC applications. Our detection algorithm consists of two main steps: gradient driven hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, possible target locations are hypothesized. This step uses an adaptive range-dependant threshold and symmetry for gradient maxima localization. Appearance-based hypothesis validation verifies those hypothesis using AdaBoost for classification with illumination independent classifiers. The monocular system was tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, varying lightening conditions), illustrating good performance.
Vehicle detection combining gradient analysis and AdaBoost classification
This paper presents a real-time vision-based vehicle's rear detection system using gradient based methods and Adaboost classification, for ACC applications. Our detection algorithm consists of two main steps: gradient driven hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, possible target locations are hypothesized. This step uses an adaptive range-dependant threshold and symmetry for gradient maxima localization. Appearance-based hypothesis validation verifies those hypothesis using AdaBoost for classification with illumination independent classifiers. The monocular system was tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, varying lightening conditions), illustrating good performance.
Vehicle detection combining gradient analysis and AdaBoost classification
Khammari, A. (Autor:in) / Nashashibi, F. (Autor:in) / Abramson, Y. (Autor:in) / Laurgeau, C. (Autor:in)
01.01.2005
482398 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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