In this paper we review a number of methods used in video surveillance applications in order to detect and classify threats. Moreover, the use of those methods in wireless surveillance networks contributes to decreasing the energy consumption of the devices because it reduces the amount of information transferred through the network. In this paper we focus on the most popular object extraction and classification methods that are used in both wired and wireless surveillance applications. We also develop an application for identification of objects from video data by implementing the selected methods and demonstrate the performance of these methods on pre-recorded videos using the outputs of this application.
Özetçe-Bu makalede, kablosuz çoklu ortam sensor ağlarında, kamera tarafından tespit edilen hedeflerin personel katkısına gerek olmadan otomatik olarak tanımlaması amacıyla uygulanacak işlemler incelenmiştir. Çalışma kapsamında video sensörler tarafından elde edilen çoklu ortam verisinden hedefin sınıflandırması amacıyla çıkarılacak özellikler ve bu özelliklerden hedefin tipinin belirlenmesi için uygulanacak yöntemler önerilmektedir. Önerilen yöntemlerin kullanımı Matlab programı ile geliştirilen bir uygulama ile test edilmiş ve sonuçları sunulmuştur.
Anahtar Kelimeler -Video; Sensör; Özellik Ç ıkarımı;
Sınıflandırma.Abstract-In this paper, it is investigated the processes for automatic identification of the targets without personnel intervention in wireless multimedia sensor networks. Methods to extract the features of the object from the multimedia data and to classify the target type based on the extracted features are proposed within the scope of this study. The success of the proposed methods are tested by implementing a Matlab application and the results are presented in this paper
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