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.Automatic Vehicle Classification Using Learning-based ComputerVision and Fuzzy LogicJailson A. de Brito Jr.Departamento de Ciência da ComputaçãoInstituto de MatemáticaUniversidade Federal da Bahiajailson@dcc.ufba.brLuis Edmundo Prado de CamposLaboratório de Geotecnia - DCTMEscola PolitécnicaUniversidade Federal da Bahialedmundo@ufba.brAbstractIn this paper, a vision-based system for traffic monitoringis presented. In a frame by frame processing, eachvehicle is detected and extracted from video acquisition.With OpenCV library[4] pattern recognition functions,the vehicles axles are recognized. Then with a fuzzy controller,each vehicle is classified according to it’s axlesconfiguration. Obtained classification precision is highenough to provide good results.1. IntroductionIn recent years, issues related with defective roadshave become a significant problem. An action thathelps to offer roads maintenance is the traffic monitoringWith a periodic traffic monitoring collecting informationslike quantity, direction and composition of thevehicle flow that pass in one or more selected pointsin a road system in a specific time interval, is possibleworking to determine evaluation of capacity, causesof traffic congestion and high levels of accidents as wellas pavement maintenance and projects of new lanes,among others improvements.A proceeding usually used in traffic engineering tocollect traffic data is called direct observation, that consistsin analyzing the traffic behavior as they are, withoutdisturbing it. Usually this task is made by an humanobserver which cannot work under certain weatherconditions and long periods of time. Many research effortshave been made in this area, trying to find goodtraffic monitoring systems, but still there is room forimprovements.A vision-based system is promising since it offers advantageslike better accuracy than no automatic observationsand lower costs comparing with some commercialsensors-based systems.A successful vision system for traffic monitoring applicationmust meet the following requirements: detectall vehicles; correctly detect all types of road vehicles;work under a wide range of traffic conditions - lighttraffic, congestion, varying speeds, varying weatherconditions and different lanes.2. Axles-based ClassificationAccording to DNIT vehicles classification [3], theclass is determined by it’s axles configuration.Figure 1. Example of vehicle adopted in DNITclassification.Each class has a quantity of axles and distances betweenaxles well defined in a certain range. Then is pos-243
WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.sible to estimate how much weight the pavement is receivingfor each vehicle, see figure 1.3. Proposed systemAiming to find an efficient solution with low cost,so it could be widely used, the proposed traffic monitoringwas divided in some modules described as follow.This system is fully programmed in Python language.3.1. Acquisition systemThe first step consist in perform a segmentation ofeach vehicle from background and others vehicle, thenis possible detecting all vehicles, see figure 2.axles which were not recognized. This training canspend some days even in high performance computers.Once all vehicles classes are classified correctly, thistraining will be not necessary anymore.The classification training consists in define somerules about the classes and it’s axles configurations ina fuzzy controller. Using fuzzy logic to do this task allowsgood results, although working under impreciseinformations about the distances of axles of each vehicle.3.3. Reporting systemAfter all processing tasks, the results should be plottedin a functional reports format, offering characteristicsabout the traffic in the observed road, like numberof vehicles by classes and time. This kind of reportis also useful to help improving the quality of roads.4. Vehicles segmentationFigure 2. Vehicle captured by acquisition systemRegions in screen which probably contains vehiclesare found using a difference between two frames technique,since they are in movement. After a thresholdfilter is applied, then a blur filter. This combinationof filters provide a resultant image that can be betterused with algorithm to find contours.So, moving objects are tracked from it’s contoursfound with Canny algorithm for edge detection[2], seefigure 3. Then, frames which objects are at the centerof screen are stored. These frames are determinedfrom the number of necessary frames to the tracked objectcross the capturing area.The acquisition software must work in real time, andshould requires a minimum of hardware to work, becauseit’s equipment should be placed in a road edgewith restricted or no security. So, the segmentation algorithmused should have low cost of computer processing.3.2. Training and Classification systemHere we have a set of applications which make possiblethe training of a classifier from information aboutaxles and vehicles classes. The training is divided intwo parts: axles detection and classification from axlesconfiguration.The axles detection training is a task that must bedone times to times providing new information aboutFigure 3. Segmentation of vehicle area244
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Automatic Pattern Recognition of Bi
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