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İHA Görüntülerinde Hareket Tabanlı Nesne Sınıflandırması

Video görüntülerinden hareket algılama başta güvenlik olmak üzere çeşitli alanlarda farklı amaçlarla kullanılmaktadır. Ancak hareketli nesnelerin tespitinde bazı zorluklarla karşılaşılabilinmektedir. Özellikle kamera hareket halindeyken hareket algılama zorlaşmaktadır. Bu çalışmada, video görüntülerinden hareketli nesnenin tespit edilmesi ve tespit edilen bu nesnenin sınıflandırılması amaçlanmaktadır. Hareketli nesnenin tespitinde iki farklı yöntemin bir arada kullanılması önerilmektedir. Birinci yöntemde hareketli kısımları kamera hareketinden ayırmak için görüntü üzerinde referans noktaları belirlenir. Bu noktalar, video görüntüsündeki ardışık kareler boyunca optik akış ile takip edilmektedir. İzlenen bu noktalardan hareket vektörleri oluşturulmaktadır. Eğim ve uzunluk açısından diğerlerinden farklılaşan vektörler hareketli nesneye ait vektörler olduğu tespit edilmiştir. Daha sonra ikinci yöntemde kareler arası dönüşüm matrisi hesaplanarak arka plan stabilizasyonu ile video görüntüsünden hareket tespiti yapılmıştır. Son olarak bu iki yöntemin kesiştirilerek hareketli olduğu tespit edilen bölgeler, önceden eğitilmiş VGG16 modeli ile bir sınıflandırıcı katman eklenerek sınıflandırılmaya çalışılmıştır. Çalışma ile VIVID Veri Seti üzerinde hareket eden nesnelerin doğru bir şekilde tespit edilip sınıflandırılması mümkün olmuştur.

Anahtar Kelimeler:

Hareketli Nesne Tespiti, Nesne Takibi, Derin Öğrenme, Anomali Tespiti

Motion-Based Object Classification on UAV Images

Motion detection from video images is used for different purposes in various fields, especially in security. However, some difficulties may be encountered in detecting moving objects. Especially when the camera is moving, motion detection becomes difficult. This study, it is aimed to detect the moving object from the video images and to classify this detected object. It is recommended to use two different methods together for detecting the moving object. In the first method, reference points are determined on the image to separate the moving parts from the camera movement. These points were followed by optical flow along successive frames in the video image. Motion vectors are created from these traced points. The vectors that differed from the others in terms of slope and length were those belonging to the moving object. Then, in the second method, by calculating the transformation matrix between the frames, motion detection from the video image was performed with background stabilization. Finally, the parts that these two methods detected as moving were tried to be classified by adding a classifier layer to the pre-trained VGG16 model. With the study, moving objects on the VIVID Dataset could be detected and classified correctly.

Keywords:

Moving Object Detection, Object Tracking, Deep Learning, Anomaly Detection,

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