NDLI: Vision-Based Forward-Looking Traffic Scene Analysis Scheme

Content Provider	IEEE Xplore Digital Library
Author	Jyh-Yeong Chang Chien-Wen Cho
Copyright Year	2007
Description	Author affiliation: Nat. Chiao Tung Univ., Hsinchu (Jyh-Yeong Chang; Chien-Wen Cho)
Abstract	Vision-based driver assistant systems are very promising in intelligent transportation system (ITS); however, algorithms capable of describing traffic scene images are still very difficult to date. This paper proposes a system which can segment forward-looking road scene image into natural elements and detect front vehicles. First, the scene analysis system deals with scene segmentation and natural object labeling of forward-looking images. By the use of fuzzy adaptive resonance theory (ART) and fuzzy inference techniques, the scene analysis task is accomplished with tolerance to uncertainty, ambiguity, irregularity, and noise existing in the traffic scene images. Secondly, the proposed system can detect the front vehicles and utilize a bounding box shape to further refine the segmentation result. Compared with conventional approaches, the proposed scheme can analyze forward-looking traffic scenes and yield reliable and efficient segmentation results. The validity of the proposed scheme in car detection was verified by field-test experiments. The traffic scene segmentation and front vehicle detection are successful.
Starting Page	56
Ending Page	61
File Size	532599
Page Count	6
File Format	PDF
ISBN	1424410673
ISSN	19310587
DOI	10.1109/IVS.2007.4290091
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-13
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Layout Vehicle detection Image segmentation Intelligent transportation systems Road vehicles Labeling Resonance Subspace constraints Uncertainty
Content Type	Text
Resource Type	Article

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