방송공학회논문지 (Journal of Broadcast Engineering)

  • 제23권3호
  • /
  • Pages.409-420
  • /
  • 2018
  • /
  • 1226-7953(pISSN)
  • /
  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
권호 표지
DOI QR코드

DOI QR Code

움직임 벡터 기반 파티클 필터를 이용한 비트스트림 상에서의 객체 추적

Object Tracking on Bitstreams Using a Motion Vector-based Particle Filter

  • 투고 : 2017.11.13
  • 심사 : 2018.04.02
  • 발행 : 2018.05.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 비트스트림 상에서 객체 추적을 위한 움직임 벡터 기반 파티클 필터(Motion Vector-based Particle Filter: MVPF)와 이를 이용한 객체 추적 시스템을 제안한다. MVPF는 일반적인 파티클 필터의 전이 모델과 관측 모델에 움직임 벡터를 사용하여 파티클의 개수를 유지하면서 정확도를 향상시킨다. 제안하는 객체 추적 시스템에서는 비트스트림에서 추출한 움직임 벡터의 히스토그램을 이용하여 객체의 상태를 예측한다. 제안하는 객체 추적 방법의 성능 평가를 위하여 MPEG 시험 영상과 VOT2013 영상에 적용하였을 때 기존 방법들보다 정확도, F-Measure, IOU(Intersection Of Union) 측면에서 평균적으로 각각 약 30%, 17%, 17% 증가하였다. 주관적 성능 평가를 위하여 추적결과를 박스(box) 형태로 표시하여 비교하였을 때 제안하는 방법이 모든 시험 영상에 대하여 기본 방법들보다 강인하게 객체를 추적한다.

In this paper, we propose a Motion Vector-based Particle Filter(MVPF) for object tracking on bitstreams and a object tracking system using the MVPF. The MVPF uses motion vectors to both the transition and the observation models of a general particle filter to improve the accuracy while maintaining the number of particles. In the proposed object tracking system, the state of the target object can be predicted using the histogram of motion vectors extracted from the bitstream. In terms of precision, F-measure and IOU(Intersection Of Union), the proposed method is about 30%, 17%, and 17% better on average, respectively, in MPEG test sequences and VOT2013 sequences. Furthermore, When the tracking results are displayed in box form for subjective performance evaluation, the proposed method can track moving objects more robust than the conventional methods in all test sequences.

키워드

참고문헌

  1. T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, "of the H.264/AVC video coding standard," IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560-576, July 2013. https://doi.org/10.1109/TCSVT.2003.815165
  2. G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, "Overview of the High Efficiency Video Coding (HEVC) standard," IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1648-1667, Dec. 2012.
  3. V. Mezaris, I. Kompatsiaris, N. V. Boulgouris, and M. G. Strintzis, "Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval," IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 5, pp. 606-621, May 2004. https://doi.org/10.1109/TCSVT.2004.826768
  4. C. Käs and H. Nicolas, "An approach to trajectory estimation of moving objects in the H.264 compressed domain," Proceeding of 3rd Pacific Rim Symp. Adv. Image Video Technol., pp. 318-329, 2009.
  5. G. R. Bradski and J. W. Davis, "Motion segmentation and pose recognition with motion history gradients," Mach. Vision Appl., vol. 13, no. 3, pp. 174-184, Jul. 2002. https://doi.org/10.1007/s001380100064
  6. S. Treetasanatavorn, U. Rauschenbach, J. Heuer, and A. Kaup, "Bayesian method for motion segmentation and tracking in compressed videos," in Proceeding of 27th DAGM Conf. Pattern Recognit., pp. 277-284, 2005.
  7. W. Zeng, J. Du, W. Gao, and Q. Huang, "Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model," Real-Time Imaging, vol. 11, no. 4, pp. 290-299, Aug. 2005. https://doi.org/10.1016/j.rti.2005.04.008
  8. S. H. Khatoonabadi and I. V. Bajic, "Video object tracking in the compressed domain using spatio-temporal Markov Random Fields," IEEE Trans. on Image Process., vol. 22, no. 1, pp.300-313, Jan. 2013. https://doi.org/10.1109/TIP.2012.2214049
  9. D. Park, D. Lee, and S. Oh, "Object tracking in HEVC bitstreams," Journal of Broadcast Engineering, vol. 20, no. 3, pp.449-463, May 2015. https://doi.org/10.5909/JBE.2015.20.3.44
  10. D. Lee, S. Yoon and S. Oh, "Visual Tracking in HEVC Compressed Videos," IEEJ Trans. on Electrical and Electronic Engineering, vol. 11, no. 2, pp.142-143, Dec. 2016.
  11. S. Giil, J. T. Meyer, T. Schierl, C. Hellge, and W. Samek, "Hybrid video object tracking in H.265/HEVC video streams," IEEE Int. conf on MMSP, Sept. 2016.
  12. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp., "A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2 pp. 174-188, 2002. https://doi.org/10.1109/78.978374
  13. D. B. Rubin, "Comment : A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest : The SIR Algorithm," Journal of the American Statistical Association, vol. 82, no. 398, pp. 542-543, 1987.
  14. Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, "Online object tracking: A benchmark," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2411-2418, 2013.