DOI QR코드

DOI QR Code

Automatic Video Chromakeying Generation Technology Using Background Modeling

배경 모델링을 이용한 비디오 크로마키 생성기법

  • 유길상 (고려대학교 정보창의교육연구소)
  • Received : 2021.08.20
  • Accepted : 2021.10.20
  • Published : 2021.10.28

Abstract

In online meetings and classes using webcams, the chromakey technique is a very necessary part to produce content. We proposed a technology that enables background synthesis without using a cloth for chromakey. The proposed method consists of three steps: an HSI image conversion step, a step of detecting a region changed from a background, and a step of replacing the background region with a chromakey and applying it. In the input video, the block average image of each frame is calculated, and the difference between the block average image of the background image and the block average image of the input image is used to detect the change area. The developed chromakey effect technology uses a technique of acquiring a background image without an object from a single camera and extracting only an object by distinguishing the moving object and the background. The proposed method is not only capable of processing even if the background has a variety of colors, but also has the seamless processing of the boundary lines of objects.

웹캠을 이용한 온라인 회의 및 수업에서 크로마키 기법을 이용한 콘텐츠 제작은 중요한 기법중의 하나이다. 본 연구에서는 크로마키 배경을 사용하지 않고 배경 합성이 가능한 기술을 제안하였다. 제안하는 방법은 HSI 이미지 변환 단계, 배경에서 변경된 영역을 감지하는 단계, 배경 영역을 크로마키로 대체하여 적용하는 단계의 3단계로 구성된다. 입력 영상에서 각 프레임의 블록 평균 영상을 계산하고, 배경 영상의 블록 평균 영상과 입력 영상의 블록 평균 영상의 차이를 이용하여 변화 영역을 검출한다. 개발된 크로마키 효과 기술은 하나의 카메라에서 물체가 없는 배경 이미지를 획득하고 움직이는 물체와 배경을 구분하여 물체만 추출하는 기술을 사용하였다. 실험결과, 제안한 방법은 배경색이 다양한 경우에도 처리가 가능할 뿐만 아니라 물체의 경계선을 매끄럽게 처리할 수 있어서 현장에서 쉽게 적용할 수 있을 것으로 기대할 수 있다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A1A01064580).

References

  1. A. Yamashita, H. Agata & T. Kaneko. (2008). Every color chromakey. 2008 19th International Conference on Pattern Recognition. 1-4. DOI : 10.1109/ICPR.2008.4761643.
  2. S. R. Rodrigues, A. C. Sementille, I. Aparecido & J. R. Ferreira. (2007). The Generation of Scenes in Mixed Reality Environments using the Chromakey Technique. 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). 296-7. DOI : 10.1109/ICAT.2007.30.
  3. J. T. Glenn, M. D. Raymond, A. C. Jaime, L. L. Danielle. (2020). Activity analysis of thermal imaging videos using a difference imaging approach. Journal of Thermal Biology, 91, 102611. DOI : 10.1016/j.jtherbio.2020.102611.
  4. C. Yang, J. Gao & F. Chen. (2012). Embedded moving image detection based on background subtraction and finite difference method. 2012 5th International Congress on Image and Signal Processing, Chongqing, 68-71. DOI : 10.1109/CISP.2012.6469790.
  5. A. Ajam, M. Forghani, M. M. AlyanNezhadi, H. Qazanfari & Z. Amiri. (2019). Content-based Image Retrieval Using Color Difference Histogram in Image Textures. 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). 1-6. DOI : 10.1109/ICSPIS48872.2019.9066062.
  6. L. Liu & J. Ding. (2018). Target Detection and Extraction Based on Motion Attention Model. 2018 Chinese Automation Congress (CAC). 2018, 1919-22. DOI : 10.1109/CAC.2018.8623780.
  7. G. A. Khaskheli, Z. A. Rajper, Q. A. Mangi, R. S. Hussain & A. H. Shar. (2020). A mediation analysis of social media marketing between the relationship of entrepreneurial marketing strategies and the performance of small & medium enterprises in Pakistan. Indian Journal of Science and Technology, 13(29), 2024-34. DOI : 10.17485/IJST/v13i29.1035
  8. B. Naik, P. B. Dash & J. Nayak. (2020) Extra-tree learning based Socio-economic factor analysis and multi-class adaptive boosting meta-estimator for prediction of agricultural productivity. Indian Journal of Science and Technology, 13(29), 2081-101. DOI : 10.17485/IJST/v13i29.839
  9. V. Ivan, G. P. Ploger & D. S. Johannes. (2019). Virtual reality for animal navigation with camera-based optical flow tracking. Journal of Neuroscience Methods, 327, 108403. DOI : 10.1016/j.jneumeth.2019.108403.
  10. M. Piccardi. (2004). Background subtraction techniques: a review/ Systems, Man and Cybernetics. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583). 4:3099-104. DOI : 10.1109/ICSMC.2004.
  11. K. Singh, A. K. Verma & M. Singh. (2020). Higher order Emden-Fowler type equations via uniform Haar Wavelet resolution technique. Journal of Computational and Applied Mathematics, 376, 112836. DOI : 10.1016/j.cam.2020.112836.
  12. Y. Kong, X. Wang & Y. Cheng. (2018). Spectral-Spatial Feature Extraction for HSI Classification Based on Supervised Hypergraph and Sample Expanded CNN. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin, 11(11), 4128-4140. DOI : 10.1109/JSTARS.2018.2869210.
  13. S. Lingfei, F. Ying, H. Hua & C. Yufeng. (2019). Fast HSI super resolution using linear regression. IET Image Processing, 13(10), 1671-79. DOI : 10.1049/iet-ipr.2018.5475.
  14. A. R. Smith. (1978). Color Gamut transform pairs, ACM SIGGRAPH Computer Graphics, 12(3), 12-19. DOI : 10.1145/965139.807361.
  15. G. S. Yoo & H. C. Kim. (2016). Embedding QR code in the wavelet domain of image for metadata hiding. Indian Journal of Science and Technology, 9(29), 1-10. DOI : 10.17485/ijst/2016/v9i29/94759