Reliable Detection of Overtaking Vehicles Using Robust Information Fusion
Ying Zhu
Comaniciu, D.
Pellkofer, M.
Koehler, T.
Dept. of Real-Time Vision & Modeling, Siemens Corporate Res. Inc., Princeton, NJ;
This paper appears in: Intelligent Transportation Systems, IEEE Transactions on
Publication Date: Dec. 2006
Volume: 7,
Issue: 4
On page(s): 401-414
Location: Tokyo, Japan,
ISSN: 1524-9050
INSPEC Accession Number: 9213130
Digital Object Identifier: 10.1109/TITS.2006.883936
Current Version Published: 2006-12-04
Abstract
Early detection of overtaking vehicles is an important task for vision-based driver assistance systems. Techniques utilizing image motion are likely to suffer from spurious image structures caused by shadows and illumination changes, let alone the aperture problem. To achieve reliable detection of overtaking vehicles, the authors have developed a robust detection method, which integrates dynamic scene modeling, hypothesis testing, and robust information fusion. A robust fusion algorithm, based on variable bandwidth density fusion and multiscale mean shift, is introduced to obtain reliable motion estimation against various image noise. To further reduce detection error, the authors model the dynamics of road scenes and exploit useful constraints induced by the temporal coherence in vehicle overtaking. The proposed solution is integrated into a monocular vision system onboard for obstacle detection. Test results have shown superior performance achieved by the new method
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