Road Edge Recognition Using the Stripe Hough Transform From Millimeter-Wave Radar Images

IEEE Transactions on Intelligent Transportation Systems(2015)

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摘要
Millimeter-wave (MMW) radar, which is used for road feature recognition, has performance that is superior to optical cameras in terms of robustness in different weather and lighting conditions, as well as providing ranging capabilities. However, the signatures of road features in MMW radar images are quite different from that of optical images, and even physically continuous features, such as road edges, will be presented as a set of bright points or spots distributed along the roadside. Therefore, discrimination of the radar features is of paramount importance in automotive imaging systems. To tackle this problem, an approach called the stripe Hough transform (HT) is introduced in this paper, allowing enhanced extraction of the geometry of the road path. The performance of the approach is demonstrated by comparison of extracted features from MMW images with the real geometry of the road and with the results of processing by classical HT.
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关键词
optical camera,millimeter-wave (mmw) radars,weather condition,road feature recognition,traffic engineering computing,hough transform (ht),road features,road edge recognition,feature extraction,automotive imaging system,object recognition,millimeter-wave radar image,lighting condition,mmw radar image,road traffic,road path geometry extraction,stripe hough transform,radar imaging,adaptive cruise control (acc),ranging capability,hough transforms,gray scale,laser radar,algorithms,edge detection,image processing,radar
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