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Paper Count: 30184
Effective Traffic Lights Recognition Method for Real Time Driving Assistance Systemin the Daytime
Abstract:This paper presents an effective traffic lights recognition method at the daytime. First, Potential Traffic Lights Detector (PTLD) use whole color source of YCbCr channel image and make each binary image of green and red traffic lights. After PTLD step, Shape Filter (SF) use to remove noise such as traffic sign, street tree, vehicle, and building. At this time, noise removal properties consist of information of blobs of binary image; length, area, area of boundary box, etc. Finally, after an intermediate association step witch goal is to define relevant candidates region from the previously detected traffic lights, Adaptive Multi-class Classifier (AMC) is executed. The classification method uses Haar-like feature and Adaboost algorithm. For simulation, we are implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and rural roads. Through the test, we are compared with our method and standard object-recognition learning processes and proved that it reached up to 94 % of detection rate which is better than the results achieved with cascade classifiers. Computation time of our proposed method is 15 ms.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328958Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2374
 Zehang Sun, "On-Road Vehicle Detection: A Review", IEEE Transation on pattern analysis and machine intelligence, Vol. 28, No. 5, pp 694-711, 2006.
 U. Franke, D. Gavrila, S. Goerzig, F. Lindner, F. Paetzold, C. Woehler, "Autonomous Driving Goes Downtown", IEEE Intelligent Systems, Vol. 13, no. 6, pp. 40-48, 1998.
 Zhuowen Tu and Ron Li, "Automatic recognition of civil infrastructure objects in mobile mapping imagery using a markov random field model", ISPRS vol. XXXIII, Amsterdam, 2000.
 Kimura F., Takahashi T., Mekada, Y., Ide I., Murase H., Miyahara T., Tamatsu Y.: "Measurement of Visibility Conditions toward Smart Driver Assistance for Traffic Signals" Intelligent Vehicles Symposium, 2007 IEEE.
 Hyun-Koo Kim, Ho-Youl Jung, and Ju H. Park, "Vehicle Detection for Adaptive Head-Lamp Control of Night Vision System", Journal of Institute of Embedded Engineering of Korea (IEMEK), Vol. 6, No. 1, pp. 8-15, 2011. 02.
 Paisitkriangkrai. S, Shen. C, Zhang. J, "Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features", Circuits and Systems for Video Technology, IEEE Transactions, vol 18, pp. 1140-1151, Aug 2008.
 Feng Tang, Crabb. R, Hai Tao, "Representing Images Using Nonorthogonal Haar-Like Bases", Pattern Analysis and Machine Intelligence, IEEE Transactions, vol 29, pp. 2120-2134, Dec. 2007.
 Chen. Q, Georganas. N. D, Petriu. E. M, "Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar", Instrumentation and Measurement, IEEE Transactions, vol 57, pp. 1562-1571, Aug 2008.
 Nishimura. J, Kuroda. T, "Low cost speech detection using Haar-like filtering for sensornet, Signal Processing", 9th International Conference (ICSP 2008), pp. 2608-2611, Oct. 2008.
 Yoav Freund, Robert E. Schapire. "A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting", 1995.
 T. Zhang, "Statistical behavior and consistency of classification methods based on convex risk minimization", Annals of Statistics 32 (1), pp. 56-85, 2004.
 Guruswami, V., Sahai, A., "Multiclass learning, boosting, and error-correcting codes". In: Proc. 12th Annual Conf. Computational Learning Theory, Santa Cruz, California, pp. 145-155, 1999.
 Allwein, E.L., Schapire, R.E., Singer, Y., "Reducing multiclass to binary: a unifying approach for margin classifiers", J. Mach. Learn, Res. 1, pp. 113-141, 2000.