III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

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WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.For vehicles with great length, two methods are beingexperimented in this first case. One solution is theuse of wide angle lens that allows detecting in a singleframe vehicles with great length. Another approach isthe use of a combinations of sequential frames methodfor those vehicles longer than the capturing area.This task is made with a fuzzy controller with rulesfor each class of vehicle. Each rule has as inputs: thenumber of axles and the distance between them, see figure5; and the vehicle class as output.5. Axles detectionDetecting axles is a task that falls in patterns recognitionarea. The haartraining functions set[1] providedwith OpenCV library were used to do this.OpenCV uses such a statistical approach for objectdetection. This method uses simple Haar-like features(so called because they are computed similar to the coefficientsin Haar wavelet transforms) and a cascade ofboosted tree classifiers as a statistical model [9]. Thismethod is tuned and primarily used for face detection.Therefore, a classifier for an arbitrary object class, likevehicles tires, can be trained and used in exactly thesame way.The cascade training was done using about 7000 imagesof positives, images containing exclusively vehiclestires, and about 3000 images of negatives, images notcontaining vehicles tires.Figure 5. Example of distance between axles inputcode.The use of a fuzzy controller allows that we workunder imprecise information, so we just lead the inputof distance between axles as not precise adjectives likelittle, medium and big instead of numeric values. Eachclass of vehicle has a specific rule, see figure 6.Figure 4. Detected axles are circledThen is possible to find how much axles the vehiclehave, the diameter of the tires, and distances betweenthem, see figure 4.6. ClassificationWith the contour of the vehicle, the number of axlesand the distance between them is possible to determinein which class the vehicle is.Figure 6. Example of rule code.245
WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.If no axles are detected then we assume that the detectedobject is not a vehicle, so it is removed from theacquired vehicles set database.7. Results[8] S. O. Rezende. Sistem as Inteligentes - Fundam entos eAplicações.Manule, São Paulo, 1 edition, 2002.[9] P. Viola and M. J. Jones. Rapid object detection using aboosted cascade of simple features. IEEE CVPR, 2001.Our first preliminary evaluating tests indicate thatthe proposed system has a good precision and has greatvalue. Tests were made with captured videos with a notsignificant number of vehicle to validate this system asit should be.As a next-step action, the system should be firedto a set of massive tests, that will make it possible tosee what can be done to decrease mistakes done by theclassifier and increasing precision, as well as the systemreliability.These conclusive tests should be presented in a futurework, since a validation of a system like the proposedin this work requires more time, because the systemshould be evaluated under a wide range of trafficconditions - light traffic, congestion, varying speeds,varying weather conditions and different lanes.8. ConclusionAiming at present a good solution to traffic monitoring,a vision-based system offers advantages like goodaccuracy and low cost.The results show that the classification precision ishigh and the instrument has great value to be widelyused.A validation of the proposed system is still requiredand will be presented in a posterior work, comparingwith others solution in same area.References[1] G. Bradski, A. Kaehler, and V. Pisarevsky. Learningbasedcomputer vision with intel’s open source computervision library. IntelTechonology Journal, 9:14, 2005.[2] J. Canny. A computational approach to edge detection,.IEEE Trans.on Pattern Analysis and M achine Intelligence,8:6, 1986.[3] D. N. de Infra-Estrutura de Transportes. M anualde E s-tudosde Tráfego. Rio de Janeiro, 2006.[4] Intel. Open source computer vision library.http://www.intel.com/research/mrl/research/opencv.[5] F. Keissarian. Computer vision - based imaging systemsfor road traffic monitoring. In A nnualResearch C onferenceatUAEUniversity, 2002.[6] I. M. Monteiro. Lógica fuzzy. Instituto de Informática,Universidade Federal do Rio Grande do Sul, 2007.[7] S. Pinto and E. Preussler. Pavimentação rodoviária: conceitosfundamentais sobre pavimentos flexíveis. Rio deJaneiro, 2002.246
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