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İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi

Sensörler ile donatılmış derinlik kamera cihazlarının maliyetlerinin ekonomik olması nedeniyle, günümüzde kullanım alanları artmakta ve yaygınlaşmaktadır. Bu çalışmada bu tür cihazların en çok kullanılanlarından biri olan Kinect cihazından elde edilen veriler üzerinde, Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı yöntemleri birlikte kullanılarak yeni bir hareket tanıma yöntemi geliştirilmiştir. Geliştirilen yöntem günlük hareketlerin yer aldığı veri setinde test edilmiş ve %98.15 oranında bir başarı ile günlük hareketler tanınabilmiştir

A New Gesture Recognition System Using Weighted Dynamic Time Warping and Symbolic Aggregation Approximation Methods on Skeleton Data

Nowadays, the usage areas of depth cameras which equipped with sensors are increasing and growing up extensively, because of their economic prices. In this study, a new gesture recognition method is developed by combining Dynamic Time Warping and Symbolic Aggregation Approximation methods on data obtained from a Kinect device which is one of the most widely used among such devices. The developed method has been tested in the data set where the daily movements recorded in and they can be recognized with a success rate of 98.15%.

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