GPS Destekli Bölütlenmiş Arka Plan Modellemesi İle Otonom Kara Araçlarından Dinamik Nesnelerin Tespiti

Bu çalışmada insansız kara araçlarının rotaları üzerinde bulunan hareketli nesneleri bilgisayar görme sistemleri kullanarak tespit edebilmesi için küresel konumlandırma sistemi destekli bir sistem geliştirilmiştir. Gerçek zamanlı çalışma öncesinde rota üzerinde belirlenen tüm koordinatlarda yalın arka planı temsil eden birer referans görüntü modeli oluşturulmuştur. Modelin oluşturulmasında 160x160 piksellik bloklar kullanılmıştır. Bu model elde edildiği koordinat bilgisi ile ilişkilendirilerek bilgisayar sistemi veri tabanında tutulur. Mobil aracın gerçek zamanlı hareketi sırasında araç belirlenen koordinatlara geldiği anda yakalan anlık görüntüler aynı yöntem ile modellenir. Sonrasında bu anlık model ile koordinatla ilişkilendirilmiş referans model çerçeve farkı yöntemi ile karşılaştırılır. İşlem sonucunda gri seviye değerleri belirlenen eşik değerden daha yüksek olan pikseller dinamik nesneler olarak kabul edilir. Geliştirilen yaklaşım ile hareketli platformlar üzerindeki kameralar ile hareketli nesneler konumsal olarak tespit edilebilmektedir. Rota üzerindeki her bir koordinat için hafızada tutulan referans modeller sayesinde hareketli kameraların oluşturduğu gürültünün dengelenmesi için karmaşık ve yüksek maliyetli işlemlere gerek duyulmamaktadır. Yapılan deneysel çalışmalarda geliştirilen sistemin anlık arka plan imge çerçevesinde bulunan nesneleri %100 ila %72.7 doğrulukta algılayabildiği ve düşük maliyetli bilgisayar sistemleri ile %4 işlemci yükü ve 0.020 sn. çerçeve başına işlem süresi ile çalışabildiği anlaşılmıştır

GPS Assisted MovingObject Detection From Unmanned Ground Vehicles by Segmented Background Modelling

In this study a global positioning system-aided model developed to be able to detect moving objects which located on the route of unmanned ground vehicles by using computer vision systems. Before real-time application, a reference model representing a simple background image was created on all coordinates determined on the route. 160x160 pixel blocks are used to generate the models. Each model associated with its coordinate information was stored into the application database. a number of snapshots are captured and modeled using the same method during the real time movement of the vehicle when the vehicle reached the specified coordinates. Then, these instant snapshots were compared with the reference model of the associated coordinates by using interframe differencing method. End of this process, the pixels which grey levels higher than the threshold were considered as dynamic objects. In the developed approach, moving objects can be detected by the camera on the spatially moving platform. There is no need for complex and costly process to compensate for the noise generated by the moving camera with reference model are held in memory for each coordinate on the route. In the experimental study, the Object detection accuracy in the instant background image frame of the developed system is observed between 100% and 72.7%, also It was determined that low-cost computer system can be used with 0.020 sec processing time per frame and 4% CPU load

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