Uçan nesnelerin otomatik tespit ve takibi için yeni bir yaklaşım

Yıl 2019, Cilt: 25 Sayı: 5, 553 - 559, 21.10.2019

Ahmet Güngör Pakfiliz

Öz

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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