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Zoom Motion Estimation Method by Using Depth Information

깊이 정보를 이용한 줌 움직임 추정 방법

  • 권순각 (동의대학교 컴퓨터소프트웨어공학과) ;
  • 박유현 (동의대학교 컴퓨터소프트웨어공학과) ;
  • 권기룡 (부경대학교 IT융합응용공학과)
  • Received : 2012.09.24
  • Accepted : 2012.11.27
  • Published : 2013.02.28

Abstract

Zoom motion estimation of video sequence is very complicated for implementation. In this paper, we propose a method to implement the zoom motion estimation using together the depth camera and color camera. Depth camera obtains the distance information between current block and reference block, then zoom ratio between both blocks is calculated from this distance information. As the reference block is appropriately zoomed by the zoom ratio, the motion estimated difference signal can be reduced. Therefore, the proposed method is possible to increase the accuracy of motion estimation with keeping zoom motion estimation complexity not greater. Simulation was to measure the motion estimation accuracy of the proposed method, we can see the motion estimation error was decreased significantly compared to conventional block matching method.

동영상의 줌 움직임 추정은 구현이 아주 복잡하다. 본 논문에서는 줌 움직임 추정을 구현하기 위하여 깊이 카메라와 색상 카메라를 동시에 이용하는 방법을 제안한다. 깊이 카메라로부터 현재블록과 참조블록 사이의 거리 정보를 얻고, 이 거리 정보로부터 두 블록사이의 줌 비율을 계산한다. 줌 비율에 맞게 참조블록을 확대 또는 축소시켜 줌으로서 움직임 추정 차신호를 줄일 수 있다. 따라서, 제안된 방법은 줌 움직임 추정을 위한 복잡도가 크지 않으면서 움직임 추정 정확도를 높이는 것이 가능하다. 모의실험을 바탕으로 제안된 방법의 움직임 추정 정확도를 측정하였으며, 기존 블록정합 방법에 비하여 움직임 추정 오차값이 크게 감소함을 확인하였다.

Keywords

References

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  3. A Recognition Method for Moving Objects Using Depth and Color Information vol.19, pp.4, 2016, https://doi.org/10.9717/kmms.2016.19.4.681
  4. A Study on High Speed Face Tracking using the GPGPU-based Depth Information vol.17, pp.5, 2013, https://doi.org/10.6109/jkiice.2013.17.5.1119
  5. Adaptive Zoom Motion Estimation Method vol.17, pp.8, 2014, https://doi.org/10.9717/kmms.2014.17.8.915
  6. Tracking Method for Moving Object Using Depth Picture vol.19, pp.4, 2016, https://doi.org/10.9717/kmms.2016.19.4.774
  7. 깊이 영상 부호화에서 신축 움직임 추정 방법 vol.20, pp.11, 2013, https://doi.org/10.9717/kmms.2017.20.11.1711