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Jang, Seok-Woo
- Journal of the Korea Academia-Industrial cooperation Society
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- v.21 no.10
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- pp.454-460
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- 2020
In recent years, the rapid increases in video distribution and viewing over the Internet have increased the risk of personal information exposure. In this paper, a method is proposed to robustly identify areas in images where a person's privacy is compromised and simultaneously blocking the object area by blurring it while rapidly tracking it using a prediction algorithm. With this method, the target object area is accurately identified using artificial neural network-based learning. The detected object area is then tracked using a location prediction algorithm and is continuously blocked by blurring it. Experimental results show that the proposed method effectively blocks private areas in images by blurring them, while at the same time tracking the target objects about 2.5% more accurately than another existing method. The proposed blocking method is expected to be useful in many applications, such as protection of personal information, video security, object tracking, etc.
https://doi.org/10.5762/KAIS.2020.21.10.454 인용 PDF KSCI

Jeong, Min-Heuk;Yu, Yong-Hyun;Park, Sung-Jun;Hwang, Seung-Jun;Baek, Joong-Hwan
- Journal of the Korea Institute of Information and Communication Engineering
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- v.24 no.3
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- pp.384-390
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- 2020
The reverberation effect on the sound when producing movies or VR contents is a very important factor in the realism and liveliness. The reverberation time depending the space is recommended in a standard called RT60(Reverberation Time 60 dB). In this paper, we propose a scene recognition technique for automatic reverberation editing. To this end, we devised a classification model that independently trains color images and predicted depth images in the same model. Indoor scene classification is limited only by training color information because of the similarity of internal structure. Deep learning based depth information extraction technology is used to use spatial depth information. Based on RT60, 10 scene classes were constructed and model training and evaluation were conducted. Finally, the proposed SCR + DNet (Scene Classification for Reverb + Depth Net) classifier achieves higher performance than conventional CNN classifiers with 92.4% accuracy.
https://doi.org/10.6109/jkiice.2020.24.3.384 인용 PDF KSCI