@inproceedings{f5c78dbf8ea94f82b4abc4db6061a545,
title = "Derinlik Kamerasi Kullanilarak Tenis Hareketlerinin Taninmasi",
abstract = "Human actions recognition has been one of the most popular subject areas in computer vision. Recently, the usage of depth cameras which are capable of generating three dimensional data enabled more complex human actions to be recognized. In this study, the problem of tennis actions recognition using a depth camera is tackled and a three dimensional tennis actions dataset has been created. To be able to recognize each tennis action, each image is represented with the three dimensional skeletal based features. Each tennis action sample is represented by appending the features of each image residing in the signature subset created with the k-means clustering method in a time based manner. With the help of supervised multi-class support vector machine method, tennis actions have been modeled with a remarkable success.",
keywords = "computer vision, depth cameras, human actions recognition, skeletal data",
author = "Bilal Ozturk and Sahin, \{Pinar Duygulu\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",
year = "2017",
month = jun,
day = "27",
doi = "10.1109/SIU.2017.7960359",
language = "T{\"u}rk{\c c}e",
series = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",
address = "!!United States",
}