Uçan nesnelerin otomatik tespit ve takibi için yeni bir yaklaşım

Bu çalışmada yerden havaya takip görevlerinde kullanılan video sistemlerinin uçan nesneleri otomatik olarak tespit ve takip etmesi için yeni bir metot sunulmaktadır. Bu yaklaşımda uçan bir nesnenin varlığının tespiti için Standart Sapma bilgisinin kullanıldığı bir metot geliştirilmiştir. Tespit sonrası takip için ölçüm verisi takibe uygun hale getirilir, bu amaçla uçan nesnenin arka fona göre daha baskın hale gelmesi sağlanır. Hedefin takibi için gerçek zamanlı performans verebilen genlik bilgisi ilave edilmiş Etkileşimli Çoklu Model Olasılıksal Veri İlişkilendirme (EÇMOVİ-GB) algoritması kullanılmıştır. EÇMOVİ-GB algoritması temelde nokta verisi takibinde kullanılan bir algoritma olmakla birlikte bu çalışmada video takibinde kullanılabilirliği gösterilmiştir. Bu amaçla örneklenen video çerçevelerinin genlik bilgileri uygun olarak kodlanarak nokta verisi haline getirilir ve takip bu veri üzerinden gerçekleştirilir. Böylece hedefin otomatik olarak tespit edildiği, takibin başlatıldığı ve sürdürüldüğü bir algoritma geliştirilmiştir. Algoritma değişik manevra, hedef tipleri ve arka fon gürültü durumları için incelenerek, başarılı sonuçlar elde edilmiştir.

Anahtar Kelimeler:

Otomatik hedef tespiti, Video takip, Etkileşimli çoklu model, Olasılıksal modelleme

A novel approach for automatic detection and tracking of flying objects

In this study, a new method is presented to automatically detect and track flying objects through video systems that are used for surface to air tracking tasks. In this approach, a method has been developed in which Standard Deviation is used to determine the presence of a flying object. The measurement data is adapted to track, so that the flying object becomes more dominant than the background. In order to track the detected target in real time, Interacting Multiple Model Probabilistic Data Association with Amplitude Information (IMMPDA-AI) algorithm is used. Although the IMMPDA-AI algorithm is mainly a point tracking algorithm, in this study, its applicability to video tracking is shown. For this purpose, the amplitude information of the sampled video frames is encoded as point data and the tracking is performed on this data. Thus, an algorithm has been developed in which the target is automatically detected, track initiated and continued. The algorithm is evaluated for different maneuvers, target types and clutter situations, and successful results are obtained.

Keywords:

Automatic target detection, Video tracking, Interacting multiple model, Probabilistic data association,

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