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학술대회 Shot Category Detection based on Object Detection Using Convolutional Neural Networks
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저자
정덕규, 손정우, 김선중
발행일
201802
출처
International Conference on Advanced Communications Technology (ICACT) 2018, pp.36-39
DOI
https://dx.doi.org/10.23919/ICACT.2018.8323638
협약과제
17ZH9900, 오픈 시나리오 기반 프로그래머블 인터랙티브 미디어 창작 서비스 플랫폼 개발(이월액), 박종현
초록
Shot category detection is a method that extracts classified shots which comprised of several sequenced frames. These are based on object detection, which relates feature extraction of image processing. Shot category detection is helpful to utilize the neural network. Neural networks are widely generalized to our society. Artificial neural networks applied in many kinds of researches and developments, which provide convenient technologies for fundamental knowledge of deep learning. In terms of artificial neural networks, we introduce shot category detection based on object detection using convolutional neural networks (CNN). This paper also proves that CNN is efficient for supervised learning.
키워드
Artificial Intelligence, CNN, Deep Learning, Machine Learning, Object Detection, Shot Category Detection
KSP 제안 키워드
Artificial neural networks, Convolution neural network(CNN), Feature extractioN, Image processing, Object detection, Researches and developments, Supervised Learning, artificial intelligence, category detection, deep learning(DL), machine Learning