Automatic LightBeam Controller for driver assistance

Machine Vision and Applications

Volumn 22, Issue 5, 2011, Pages 819-835

  Alcantarilla, P F ^a   Bergasa, L M ^a   Jimenez P ^a   Parra, I ^a   Llorca, D F ^a   Sotelo, M A ^a   Mayoral, S S ^b  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b Research Department   (Spain)

Author keywords

Computer vision; Driver assistance systems; Head lights detection; Support vector machines; Tail lights detection

Indexed keywords

CONTROLLING VEHICLES; DRIVER ASSISTANCE; DRIVER-ASSISTANCE SYSTEMS; DRIVING CONDITIONS; IMAGE PROCESSING TECHNIQUE; LOW BEAM; NIGHT DRIVING; REAL TIME; ROAD SIGNS; STATE-OF-THE-ART METHODS; SUPPORT VECTOR; SYSTEM USE; URBAN AREAS; VIDEO SEQUENCES;

AUTOMOBILE DRIVERS; COMPUTER VISION; LIGHT SOURCES; SUPPORT VECTOR MACHINES; VIDEO RECORDING;

VEHICLES;

EID: 80053922376     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-011-0327-y     Document Type: Article

References (23)

1. U.S. Department of Transportation: Regulations of the light emissions of vehicle high beam headlamps (2007). http://www.dot.gov/.
2. Akashi, Y., Rea, M.: The effect of oncoming headlight glare on peripheral detection under a mesopic light level. In: Progress in Automotive Lighting (2001).
3. GENTEX: Vehicle lamp control (2005). http://www.patentstorm. us/patents/6947577-fulltext.html.
4. Mobileye:Adaptative headlight control (2007). http://www.mobileye-vision. com/.
5. Chen, Y.L.: Nighttime vehicle light detection on a moving vehicle using image segmentation and analysis techniques. WSEAS Trans. Comput. 8(3), 506-515 (2009).
6. stInternational Symposium on Vehicular Computing Systems, Trinity College Dublin (2008).
7. López, A., Hilgenstock, J., Busse, A., Baldrich, R., Lumbreras, F., Serrat, J.: Nighttime vehicle detection for intelligent headlight control. In: ACIVS 08: Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 113-124. Springer, Berlin (2008).
8. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the European Conference on Computational Learning Theory, pp. 23-37 (1995).
9. Alcantarilla, P.F., Bergasa, L.M., Jiménez, P., Sotelo, M.A., Parra, I., Fernández, D., Mayoral, S.S. : Night time vehicle detection for driving assistance. In: IEEE Intelligent Vehicles Symposium (IV) (2008).
10. Clanton, J.M., Bevly, D.M., Hodel, A.S.:Alow-cost solution for an integrated multisensor lane departurewarning system. IEEE Trans. Intell. Transp. Syst. 10, 47-59 (2009).
11. Parra, I., Fernández, D., Sotelo, M.A., Bergasa, L.M., Revenga, P., Nuevo, J., Ocaña, M., García, M.A.: A combination of fea- ture extractionmethods for SVMpedestrian detection. IEEE Trans. Intell. Transp. Syst. 8(2), 292-307 (2007).
12. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62-66 (1979).
13. Welch, G., Bishop, G.: An introduction to the Kalman filter. Tech. Rep., University of North Carolina at Chapel Hill, Department of Computer Science (2001).
14. Bertozzi, M., Broggi, A., Fascioli, A., Tibaldi, A.: Pedestrian localization and tracking system with Kalman filtering. In: IEEE Intelligent Vehicles Symposium (IV), pp. 584-589 (2004).
15. Dellaert, F., Thorpe, C.: Robust car tracking using Kalman filtering and Bayesian templates. In: Conference on Intelligent Transportation Systems (1997).
16. Jain, A.: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs (1986).
17. Derong, Y., Yuanyuan, Z., Dongguo, L.: Fast computation of multiscale morphological operations for local contrast enhancement. In: IEEE Medicine and Biology 27th Annual Conference (2005).
18. Lindeberg, T.: Feature detectionwith automatic scale selection. Int. J. Comput. Vis. 30(2), 77-116 (1998).
19. Dickmanns, E.D., Mysliwetz, B.D.: Recursive 3-D road and relative ego-state recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 199-213 (1992).
20. Alcantarilla, P., Sotelo, M.A., Bergasa, L.M.: Automatic daytime road traffic control and monitoring system. In: IEEE Intelligent Transportations Systems Conference (ITSC), pp. 944-949 (2008).
21. Hu, M.: Visual pattern recognition by moments. IRE Trans. Inf. Theory 8(2), 179-187 (1962).
22. Hupkens, T.M., de Clippeleir, J.: Noise and intensity invariant moments. Pattern Recognit. 16, 371-376 (1995).
23. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE. Trans. Pattern Anal. Mach. Intell. 31(12), 2179-2195 (2009).