Design and implementation of a real-time LDWS with parameter space filtering for embedded platforms
In this work, a lane departure warning system (LDWS) algorithm for embedded platforms which has restricted resources is proposed. An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usu...
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| Veröffentlicht in: | Journal of real-time image processing 2022-06, Vol.19 (3), p.663-673 |
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| creator | Selim, Erman Alci, Musa Uğur, Aybars |
| description | In this work, a lane departure warning system (LDWS) algorithm for embedded platforms which has restricted resources is proposed. An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usually require high processing power and even GPU processing power. Therefore, they are not applicable for hardware with limited resources. In this work, Hough Transform (HT)-based lane detection algorithm is applied. The vulnerability of HT-based methods against misleading images is eliminated by the proposed filtering algorithm. Main differences of the proposed filtering algorithm from the classical methods in the literature are that it is applied in the parameter space rather than the image, and it is specialized only for determining lanes. In the lane tracking stage, the K-means clustering algorithm has been modified to operate online. In this way, the parameters of the detected lane can be followed adaptively during lane changing or overtaking. Real-time test results on embedded hardware demonstrated that the processing time does not exceed 41.67 ms with an accuracy of over 91.5%. |
| doi_str_mv | 10.1007/s11554-022-01213-3 |
| format | Article |
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An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usually require high processing power and even GPU processing power. Therefore, they are not applicable for hardware with limited resources. In this work, Hough Transform (HT)-based lane detection algorithm is applied. The vulnerability of HT-based methods against misleading images is eliminated by the proposed filtering algorithm. Main differences of the proposed filtering algorithm from the classical methods in the literature are that it is applied in the parameter space rather than the image, and it is specialized only for determining lanes. In the lane tracking stage, the K-means clustering algorithm has been modified to operate online. In this way, the parameters of the detected lane can be followed adaptively during lane changing or overtaking. 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An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usually require high processing power and even GPU processing power. Therefore, they are not applicable for hardware with limited resources. In this work, Hough Transform (HT)-based lane detection algorithm is applied. The vulnerability of HT-based methods against misleading images is eliminated by the proposed filtering algorithm. Main differences of the proposed filtering algorithm from the classical methods in the literature are that it is applied in the parameter space rather than the image, and it is specialized only for determining lanes. In the lane tracking stage, the K-means clustering algorithm has been modified to operate online. In this way, the parameters of the detected lane can be followed adaptively during lane changing or overtaking. Real-time test results on embedded hardware demonstrated that the processing time does not exceed 41.67 ms with an accuracy of over 91.5%.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Cameras</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Computer Graphics</subject><subject>Computer Science</subject><subject>Filtration</subject><subject>Hardware</subject><subject>Hough transformation</subject><subject>Image Processing and Computer Vision</subject><subject>Lane keeping</subject><subject>Methods</subject><subject>Multimedia Information Systems</subject><subject>Original Research Paper</subject><subject>Parameter modification</subject><subject>Pattern Recognition</subject><subject>Platforms</subject><subject>Real time</subject><subject>Roads & highways</subject><subject>Signal,Image and Speech Processing</subject><subject>Tracking</subject><subject>Vector quantization</subject><subject>Vehicles</subject><subject>Warning systems</subject><issn>1861-8200</issn><issn>1861-8219</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMlKQzEUhoMoWKsv4CrgOpqhd8hSWicouFBxGXKTk5pyJ5MU8e1NvaI7V2f8_8P5EDpn9JJRWl1FxopiQSjnhDLOBBEHaMbqkpGaM3n4m1N6jE5i3FJaVqUoZsisIPpNj3Vvse_GFjrok05-6PHgsMYBdEuS7wCvV69P-MOnNzzqoDtIEHActQHsfJsL32-wGwKGrgFrweKx1Sk3uniKjpxuI5z9xDl6ub15Xt6T9ePdw_J6TYxgMhFNzULYytimNAJ4I5tac1ZCoW3poJSQ57UtFpJT14DJN7QWklWNsAyMq8UcXUy-YxjedxCT2g670OeTissMoCpqKfIWn7ZMGGIM4NQYfKfDp2JU7WGqCabKMNU3TLUXiUkUx_2jEP6s_1F9AezpeS8</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Selim, Erman</creator><creator>Alci, Musa</creator><creator>Uğur, Aybars</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0003-3622-7672</orcidid><orcidid>https://orcid.org/0000-0003-4479-0406</orcidid><orcidid>https://orcid.org/0000-0002-5382-3460</orcidid></search><sort><creationdate>20220601</creationdate><title>Design and implementation of a real-time LDWS with parameter space filtering for embedded platforms</title><author>Selim, Erman ; 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| subjects | Accuracy Algorithms Cameras Cluster analysis Clustering Computer Graphics Computer Science Filtration Hardware Hough transformation Image Processing and Computer Vision Lane keeping Methods Multimedia Information Systems Original Research Paper Parameter modification Pattern Recognition Platforms Real time Roads & highways Signal,Image and Speech Processing Tracking Vector quantization Vehicles Warning systems |
| title | Design and implementation of a real-time LDWS with parameter space filtering for embedded platforms |
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