This study presents a driver Traffic Training Simulator (TTS) that utilizes intelligent synthetic actors. The movements of intelligent actors are realized using a network flow graph consisting of segment nodes. The intelligent actors in traffic are capable of such objectives as vehicles and lane following. In addition, they are capable of moving according to the traffic signs and lights. The duration of traffic lights can parametrically be determined by the simulator interface. Moreover, parametric values such as weather conditions, seasons, and sunlight can be fed to the simulator as inputs. Similarly, the type of the driver's vehicle and other intelligent vehicles and their numbers can also be parametrically determined. The driver is able to drive in the heavy and light traffic conditions. Currently we are focusing on incorporating the hardware components into the system. Following the tests for the candidates with the system is expected to take place in driver training schools.
Original scientific paper Traffic jam is one of the hardest problems of the crowded cities, and it needs to be solved. In this study, the effect of the minimum speed limit signs in addition to the maximum speed signs and their locations in traffic flow has been examined by using cellular automata (CA). Urban traffic is modeled by two dimensional CA. The model includes traffic signs, traffic lights and some kinds of vehicles (such as automobiles, vans, buses, metro buses) that are often encountered in traffic. Keywords: application virtualization; cellular automata; computer graphics; computer simulation; transportation systems Modeliranje gradskog prometa mikroskopskim pristupom primjenom celularnih automataIzvorni znanstveni članak Zastoj prometa je jedan od najvećih problema prenapućenih gradova i potrebno ga je riješiti. U ovom se radu, primjenom celularnih automata (CA), uz znakove maksimalne brzine ispitivao učinak znakova minimalnog ograničenja brzine i njihova lokacija u prometu. Gradski je promet modeliran dvodimenzionalnim CA. Model uključuje prometne znakove, prometna svjetla i neke vrste vozila (automobili, furgoni, autobusi, metro autobusi) koja su česta u prometu.
ÖzetSensörler ile donatılmış derinlik kamera cihazlarının maliyetlerinin ekonomik olması nedeniyle, günümüzde kullanım alanları artmakta ve yaygınlaşmaktadır. Bu çalışmada bu tür cihazların en çok kullanılanlarından biri olan Kinect cihazından elde edilen veriler üzerinde, Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı yöntemleri birlikte kullanılarak yeni bir hareket tanıma yöntemi geliştirilmiştir. Geliştirilen yöntem günlük hareketlerin yer aldığı veri setinde test edilmiş ve %98.15 oranında bir başarı ile günlük hareketler tanınabilmiştir. A New Gesture Recognition System Using Weighted Dynamic Time Warping and Symbolic Aggregation Approximation Methods on Skeleton Data Keywords AbstractNowadays, the usage areas of depth cameras which equipped with sensors are increasing and growing up extensively, because of their economic prices. In this study, a new gesture recognition method is developed by combining Dynamic Time Warping and Symbolic Aggregation Approximation methods on data obtained from a Kinect device which is one of the most widely used among such devices. The developed method has been tested in the data set where the daily movements recorded in and they can be recognized with a success rate of 98.15%.
Özetçe-Bu çalışmada derinlik kamerasından alınan veriler kullanılarak, insanların kamera karşısında yaptıkları hareketlerin tanımlamasını gerçekleştiren bir uygulama geliştirilmiştir. Derinlik kamerası olarak Microsoft Kinect kullanılmıştır. Tanımlanması gerçekleştirilen hareketler, insanların günlük hayatta yaptıkları hareketleri barındıran Microsoft firmasına ait MSRC-12 veri kümesinden temin edilmiştir. Hareket tanıma esnasında üretilen büyük boyutlu veriler, Piecewise Aggregate Approximation (PAA) yöntemi kullanılarak azaltılmıştır. Azaltılan veriler Symbolic Aggregate approXimation (SAX) yöntemi ile sınıflandırılmıştır. Geliştirilen algoritma ile SAX'dan elde edilen veriler arasındaki benzerlik yüzdesi hesaplanmıştır. Uygulama, tanımlı bütün hareketleri doğru olarak tespit edebilmektedir. Anahtar Kelimeler -Microsoft Kinect; SAX; hareket tanıma; PAA.Abstract-In this study, an application is developed to recognize human gestures using data which was recorded by using Microsoft Kinect. The data set used in the study is MSRC-12, and it is created by Microsoft. It has several daily human gestures which were recorded from different users. Before gesture recognition process, recorded data was reduced by PAA method and then it was classified by SAX method. Symbols (which are generated by SAX) of percentage similarity is calculated by developed algorithm. The application can recognize all human gestures in dataset correctly.
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