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http://dx.doi.org/10.7471/ikeee.2020.24.2.529

A Feature Point Extraction and Identification Technique for Immersive Contents Using Deep Learning  

Park, Byeongchan (Dept. of Computer Science and Engineering, Soongsil University)
Jang, Seyoung (Dept. of Computer Science and Engineering, Soongsil University)
Yoo, Injae (Research Institute, Beyondtech Inc.)
Lee, Jaechung (Research Institute, Beyondtech Inc.)
Kim, Seok-Yoon (Dept. of Computer Science and Engineering, Soongsil University)
Kim, Youngmo (Dept. of Computer Science and Engineering, Soongsil University)
Publication Information
Journal of IKEEE / v.24, no.2, 2020 , pp. 529-535 More about this Journal
Abstract
As the main technology of the 4th industrial revolution, immersive 360-degree video contents are drawing attention. The market size of immersive 360-degree video contents worldwide is projected to increase from $6.7 billion in 2018 to approximately $70 billion in 2020. However, most of the immersive 360-degree video contents are distributed through illegal distribution networks such as Webhard and Torrent, and the damage caused by illegal reproduction is increasing. Existing 2D video industry uses copyright filtering technology to prevent such illegal distribution. The technical difficulties dealing with immersive 360-degree videos arise in that they require ultra-high quality pictures and have the characteristics containing images captured by two or more cameras merged in one image, which results in the creation of distortion regions. There are also technical limitations such as an increase in the amount of feature point data due to the ultra-high definition and the processing speed requirement. These consideration makes it difficult to use the same 2D filtering technology for 360-degree videos. To solve this problem, this paper suggests a feature point extraction and identification technique that select object identification areas excluding regions with severe distortion, recognize objects using deep learning technology in the identification areas, extract feature points using the identified object information. Compared with the previously proposed method of extracting feature points using stitching area for immersive contents, the proposed technique shows excellent performance gain.
Keywords
Immersive Content; Deep Learning; Feature Point Extracting and Matching; Piracy Judgment; OMAF;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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