Güray SONUGÜR, Simge Ayşe AKAN, Elif Ebru ÇAKI, Celal Onur GÖKÇE

Gerçek Zamanlı İnsan Davranışı Anlamaya Doğru: Optimal-Altı Bir Şekil Tanımlayıcı

Bu çalışmada insan davranışı anlama (İDA) probleminin çözümünde kullanılmak üzere özgün optimal ve optimal-altı şekil tanımlayıcıları önerilmiştir. Bu şekilde en az veri kullanımıyla en fazla davranış bilgisini sınıflandırabilmek amaçlanmıştır. Optimal şekil tanımlayıcısı başarısı yüksek olmakla beraber algoritmik karmaşıklığı yüksek olduğu için oldukça yavaş çalışmaktadır. Bu sorunu gidermek için daha hızlı çalışan bir optimal-altı tanımlayıcı önerilmiştir. Optimal-altı tanımlayıcının başarısı optimal tanımlayıcıya çok yakın olmakla beraber çok daha düşük algoritmik karmaşıklığa sahip olup çok daha hızlıdır. Sonuçlar Weizmann veri setinde denenmiş ve şekiller ve video bağlantıları ile gösterilmiştir. Veri setinden elde edilen siluet görüntü akışlarından 12 adet istatistiksel öznitelik çıkarılıp sınıflandırmada kullanılmıştır. Sınıflandırmada kullanılan Öklid uzaklığı yöntemi sayesinde oldukça hızlı sonuçlar üretilerek %92 doğruluk oranına ulaşılmıştır.

Towards Real-Time Human Behavior Understanding: A Suboptimal Shape Descriptor

In this study, two novel shape descriptors are proposed to be used in human behavior understanding problem. First is optimal shape descriptor, which has high performance but works very slow due to high algorithmic complexity. Second is suboptimal shape descriptor, performance of which is very close to optimal one, but works much more faster. Optimal means using minimum data to represent maximum knowledge. Algorithms are run on Weizmann dataset and results are shown both as figure and video link. Classification was performed using 12 statistical features extracted from the data sets' human silhouettes. An accuracy rating of 92 percent was obtained by using Euclidean distance in classification.

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