A Real-time Precrash Vehicle Detection System

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A Real-time Precrash Vehicle Detection System
— This paper presents an in-vehicle real-time monocular precrash vehicle detection system. The system acquires grey level images through a forward facing low light camera and achieves an average detection rate of 10Hz. The vehicle detection algorithm consists of two main steps: multi-scale driven hypothesis generation and appearancebased hypothesis verification. In the multi-scale hypothesis generation step, possible image locations where vehicles might be present are hypothesized. This step uses multiscale techniques to speed up detection but also to improve system robustness by making system performance less sensitive to the choice of certain parameters. Appearance-based hypothesis verification verifies those hypothesis using Haar Wavelet decomposition for feature extraction and Support Vector Machines (SVMs) for classification. The monocular system was tested under different traffic scenarios (e.g., simply structured highway, complex urban street, varying weather conditions),...
Zehang Sun, Ronald Miller, George Bebis, David DiM
Added 16 Jul 2010
Updated 16 Jul 2010
Type Conference
Year 2002
Where WACV
Authors Zehang Sun, Ronald Miller, George Bebis, David DiMeo
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