ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술대회 Edge Computing System applying Integrated Object Recognition based on Deep Learning
Cited 0 time in scopus Download 2 time Share share facebook twitter linkedin kakaostory
저자
김귀훈, 오세훈
발행일
202102
출처
International Conference on Advanced Communications Technology (ICACT) 2021, pp.1-5
DOI
https://dx.doi.org/10.23919/ICACT51234.2021.9370761
초록
With the development of personal broadcasting such as YouTube, the demand for editing the video he filmed himself is steadily increasing. Traditional media video editing solutions help edit the results of the filming. However, there is a problem that requires a lot of editing time, as people usually edit videos that are from tens to hundreds of times longer than the final result of a personal broadcasting after filming. To overcome this problem, this paper automatically classifies images with specific scenes in the whole media image editing process, and secondly proposes automatic media editing solution technology in which people intervene. In particular, personal broadcasting focuses on the use of images that include characters, specific objects, and cue sign gestures among the entire. While the existing deep learning techniques such as faces, objects and gestures are advanced, integrated recognition technologies that simultaneously deal with special requirements for editing videos are still in the early stages of research. In this paper, the automatic composite recognition technology for editing video based on deep learning is proposed. The proposed technology was implemented with python and tensorflow software based on edge computing equipment. Using actual youtube videos, it took 0.1 second to process five-person recognition, 63-food recognition, or cue sign recognition using clapping or V poses at the same time. The recognized results are divided into timestamps of the entire movie, recognition results, and locations of objects on the screen, and are output to the json file. In addition, this solution was developed on an edge computing in order to increase real-Time reliability. We expect to provide automatic video editing based on perceived json results as well as shorter editing times based on this implementation.
키워드
Automatic media editor, Context recognition, Deep learning, Edge computing, Object recognition
KSP 제안 키워드
Context Recognition, Early stages, Food Recognition, Image editing, Integrated recognition, Object Recognition, Person recognition, Real-time reliability, Traditional media, deep learning(DL), edge computing