文章基本信息

标题：A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran
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作者：Anton Satria Prabuwono ; Ismatul Maula ; Wendi Usino 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：10
DOI：10.14569/IJACSA.2018.091033
出版社：Science and Information Society (SAI)
摘要：Movement change beyond the duration of time and the variations of object appearance becomes an interesting topic for research in computer vision. Object behavior can be recognized through movement change on video. During the recognition of object behavior, the target and the trace of an object in a video must be determined in the sequence of frames. To date, the existence of object on a video has been widely used in different areas such as supervision, robotics, agriculture, health, sports, education, and traffic. This research focuses on the field of education by recognizing the movement of Quantum Maki Quran memorization through a video. The purpose of this study is to enhance the existing computer vision technique in detecting the Quantum Maki Quran memorization movement on a video. It combines the Background Subtraction method and Artificial Neural Networks; and evaluates the combination to optimize the system accuracy. Background Subtraction is used as object detection method and Back propagation in Artificial Neural Networks is used as object classification. Nine videos are obtained by three different volunteers. These nine videos are divided into six training and three testing data. The experimental result shows that the percentage of accuracy system is 91.67%. It can be concluded that there are several factors influencing the accuracy, such as video capturing factors, video improvements, the models, feature extraction and parameter definitions during the Artificial Neural Networks training.
关键词：Movement recognition; computer vision; Quran memorization movement; background subtraction; back propagation; artificial neural networks