DOI QR코드

DOI QR Code

Shot Type Detecting System using Face Detection

얼굴 검출을 이용한 숏 유형 감지 시스템

  • 백영태 (김포대학교 멀티미디어과) ;
  • 박승보 (경희대학교 경영대학)
  • Received : 2012.09.03
  • Accepted : 2012.09.24
  • Published : 2012.09.30

Abstract

In this paper, we propose the method that decides the shot types using face detection technique. The shot types, such as close-up shot, medium shot, and long shot, can be applied as useful information for understanding narrative structure of movies. The narrative structure of movie is builded by characters. Also their mental and emotional changes become inextricably bound up with them of narrative. The shot types are decided by distance between character and camera. If put together above them, shot types can be found by using detection technique of face size of characters and understand narrative of movie. To do this, we propose the methodology to detect shot type by face detecting and implement the system to do it. Additionally, we evaluate the performance of the system. The implementation system has been evaluated as 95% for close-up shot detection and 90% for medium shot detection, while 53.3% is just detected for long shots.

본 논문은 얼굴 검출을 이용한 숏의 유형을 판단하는 방법론을 제시한다. 클로즈 업 숏이나 미디엄 숏, 롱 숏과 같은 숏의 유형은 영화의 서사 구조를 파악하는 주요한 단서이다. 클로즈 업을 통해 감독은 등장인물의 감정 상태를 묘사하고 롱 숏을 통해 인물이 처한 상황이나 배경을 묘사하게 된다. 인물의 심리나 감정의 변화, 인물이 처한 상황을 묘사하는 숏의 여러 유형은 인물과 카메라의 거리에 의해 결정된다. 따라서 화면에 등장하는 인물의 얼굴 크기를 알아내어 숏의 유형을 판단할 수 있다. 이를 위해 본 논문에서는 얼굴 검출을 통해 숏의 유형을 감지하는 방법론을 제시하고 시스템으로 구현하여 성능을 평가한다. 평가실험에서 클로즈 업 숏과 미디엄 숏의 감지 성능은 95%와 90%로 비교적 높게 나타났지만 얼굴의 윤곽이 불분명한 롱 숏의 경우 53.3%로 측정되었다.

Keywords

References

  1. Roy Thompson, "Grammar of the Edit," Butterworth- Heinemann, pp. 24-43, 1993.
  2. Jonggab Kim, "The Rhetoric of Close-up: the Face of Greta Garbo," The Culture and Image, vol. 4, no. 2, pp. 5-28, December 2003.
  3. Luca Canini, Sergio Benini, and Reccardo Leonardi, "Affective Analysis on Patterns of Shot Types on Movies," Proceeding of 7th International Symposium on Image and Signal Processing and Analysis (ISPA 2011), pp. 253-258, september 2011.
  4. Heeman Lee, "Implementing Augmented Reality By Using Face Detection, Recognition And Motion Tracking," Journal of the Korea Society of Computer and Information, vol. 17, no. 1, pp.97-104, January 2012. https://doi.org/10.9708/jksci.2012.17.1.097
  5. Donghyeon Kim, Jaehyun Im, Daehee kim, Taekyung Kim, and Joonki Paik, "Face Detection and Tracking using Skin Color Information and Haar-Like Features in Real-Time Video," HCI 2009, pp. 146-149, February 2009.
  6. Joong-Gyo Jeong, Sang-Sung Park,and Dong-Sik Jang, "Real-Time face detection using the Skin color and Haar-like feature," Journal of the Korea society of computer and information, vol. 10, no. 4, pp. 113-121, September 2005,
  7. Seung-Bo Park, Heung-Nam Kim, Hyunsik Kim, and Geun-Sik Jo, "Exploiting Script-Subtitles Alignment to Scene Boundary Detection in Movie," Proceeding of IEEE International Symposium on Multimedia (ISM2010), pp. 49-56, December 2010.
  8. Eun-Kyoung Kim, HyunJu Kim, Hyun Mi Jo, and Yong-Hwan Lee, "Real-time Face Detection and Tracking using the AdaBoost Algorithm," Proceeding of Korean Institute of Information Technology Summer Domestic Conference, pp. 379-383, May 2011.
  9. OpenCV, http://opencv.org/
  10. OpenCV Swiki, http://alereimondo.no-ip.org/OpenCV

Cited by

  1. 신경회로망 기반의 관객 반응 추출 시스템 vol.20, pp.2, 2012, https://doi.org/10.9708/jksci.2015.20.2.047
  2. Comparative Study of Movie Shot Classification Based on Semantic Segmentation vol.10, pp.10, 2012, https://doi.org/10.3390/app10103390