Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Robust Object Tracking based on Kernelized Correlation Filter with multiple scale scheme

다중 스케일 커널화 상관 필터를 이용한 견실한 객체 추적

  • Yoon, Jun Han (Dept. of Computer Engineering, Seokyeong University) ;
  • Kim, Jin Heon (Dept. of Computer Engineering, Seokyeong University)
  • Received : 2018.09.10
  • Accepted : 2018.09.20
  • Published : 2018.09.30
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Abstract

The kernelized correlation filter algorithm yielded meaningful results in accuracy for object tracking. However, because of the use of a fixed size template, we could not cope with the scale change of the tracking object. In this paper, we propose a method to track objects by finding the best scale for each frame using correlation filtering response values in multi-scale using nearest neighbor interpolation and Gaussian normalization. The scale values of the next frame are updated using the optimal scale value of the previous frame and the optimal scale value of the next frame is found again. For the accuracy comparison, the validity of the proposed method is verified by using the VOT2014 data used in the existing kernelized correlation filter algorithm.

커널 상관 필터 알고리듬은 객체 추적에 대해 정확도에서 의미 있는 성과를 거두었다. 그러나 고정된 크기의 템플릿을 사용하기 때문에 추적 대상의 스케일 변화에 대처할 수 없었다. 본 논문에서는 최근접 보간법과 표준 가우시안 정규화를 이용한 다중 스케일에서의 상관 필터링 응답 값을 이용하여 프레임별로 가장 적합한 스케일을 찾아 객체를 추적하는 방식을 제안한다. 다음 프레임의 스케일 값들은 이전 프레임의 최적 스케일 값을 이용해 갱신하고 다시 해당 프레임에서의 최적의 스케일 값을 찾는다. 정확도 비교를 위해 기존 커널 상관 필터 알고리듬에서 사용된 VOT2014 데이터를 사용하여 제안된 방법의 유효성을 검증한다.

Keywords

References

  1. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," Acm computing surveys (CSUR), vol.38, no.4, pp. 13, 2006. DOI:10.1145/1177352.1177355
  2. S. Salti, A. Cavallaro, and L. D. Stefano, "Adaptive appearance modeling for video tracking: Survey and evaluation," Image Processing, IEEE Transactions on, vol.21, no.10, pp. 4334-4348, 2012. DOI:10.1109/TIP.2012.2206035
  3. Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark. In Computer vision and pattern recognition," CVPR, 2013 IEEE Conference on, pp. 2411-2418. 2013.
  4. A. Smeulders, D. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, "Visual tracking: An experimental survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, no.7, pp. 1442-1468, 2014. DOI:10.1109/TPAMI.2013.230
  5. Boddeti, V.N., Kanade, T., Kumar, B.V.: "Correlation filters for object alignment," CVPR 2013 IEEE Conference on, pp. 2291-2298, 2013. DOI:10.1109/CVPR.2013.297
  6. Galoogahi, H.K., Sim, T., Lucey, S. "Multichannel correlation filters," ICCV, pp. 4321-4328, 2013. DOI:10.1109/ICCV.2013.381
  7. Henriques, J. F., Carreira, J., Caseiro, R., Batista, J, "Beyond hard negative mining: Efficient detector learning via block-circulant decomposition," ICCV, pp. 2760-2767, 2013. DOI:10.1109/ICCV.2013.343
  8. Revaud, J., Douze, M., Cordelia, S., Jgou, H, "Event retrieval in large video collections with circulant temporal encoding," CVPR, 2013, pp. 2459-2466.
  9. D. S. Bolme, J. R. Beveridge, B. A. Draper, Lui, Y. M, "Visual object tracking using adaptive correlation filters," CVPR, pp. 2544-2550, 2010. DOI:10.1109/CVPR.2010.5539960
  10. Henriques, J. F., Carreira, J., Caseiro, R., Batista, J, "Beyond hard negative mining: Efficient detector learning via block-circulant decomposition," ICCV, pp. 2760-2767, 2013. DOI:10.1109/ICCV.2013.343
  11. M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, L. Cehovin, G. Nebehay, T. Vojir, G. Fernandez, A. Lukezic, A. Dimitriev, et al, "The visual object tracking vot 2014 challenge results," Computer Vision-ECCV 2014 Workshops, 2014, pp. 191-217.
  12. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking. Pattern Analysis and Machine Intelligence," IEEE Transactions on, vol.25, no.5, pp. 564-577, 2003. DOI:10.1109/TPAMI.2003.1195991
  13. T. Poggio and G. Cauwenberghs, "Incremental and decremental support vector machine learning," Advances in neural information processing systems, pp. 409, 2001.
  14. A. Adam, E. Rivlin, and I. Shimshoni, "Robust fragmentsbased tracking using the integral histogram," Computer vision and pattern recognition, 2006 IEEE Computer Society Conference on, vol.1, pp. 798-805, 2006. DOI10.1109/CVPR.2006.256
  15. X. Mei and H. Ling, "Robust visual tracking using L1 minimization," Computer Vision, 2009 IEEE 12th International Conference on, pp. 1436-1443, 2009. DOI:10.1109/ICCV.2009.5459292
  16. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Robust visual tracking via multi-task sparse learning," Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2042-2049, 2012. DOI:10.1109/CVPR.2012.6247908
  17. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Low-rank sparse learning for robust visual tracking," Computer Vision-ECCV 2012, pp. 470-484, 2012. DOI:10.1007/978-3-642-33783-3_34
  18. T. Zhang, S. L. C. Xu, S. Yan, B. Ghanem, N. Ahuja, and M.-H. Yang, "Structural sparse tracking," Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, pp. 150-158, 2015. DOI:10.1109/CVPR.2015.7298610
  19. B. Babenko, M.-H. Yang, and S. Belongie, "Visual tracking with online multiple instance learning," Computer Vision and Pattern Recognition, CVPR 2009. IEEE Conference on, pp. 983-990, 2009. DOI:10.1109/CVPR.2009.5206737
  20. S. Avidan, "Ensemble tracking," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.29, no.2, pp. 261-271, 2007. DOI:10.1109/TPAMI.2007.35
  21. S. Avidan, "Support vector tracking," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, no.8, pp. 1064-1072, 2004. DOI:DOI:10.1109/TPAMI.2004.53
  22. B. Vijaya Kumar, "Minimum-variance synthetic discriminant functions," JOSA A, vol.3, no.10, pp. 1579-1584, DOI:1986.10.1364/JOSAA.3.001579
  23. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," Computer Vision-ECCV 2012, pp. 702-715, 2012. DOI:10.1007/978-3-642-33765-9_50
  24. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High speed tracking with kernelized correlation filters," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.37, no.3, pp. 583-596, 2015. DOI:10.1109/TPAMI.2014.2345390
  25. T. Liu, G. Wang, and Q. Yang, "Real-time part-based visual tracking via adaptive correlation filters," Intelligence, pp. 2345-2390, 2015. DOI:10.1109/CVPR.2015.7299124
  26. C. Ma, X. Yang, C. Zhang, and M.-H. Yang, "Long-term correlation tracking," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388-5396, 2015. DOI:10.1109/CVPR.2015.7299177
  27. Henriques, J. F., Caseiro, R., Martins, P., Batista, J. "High-speed tracking with kernelized correlation filters," TPAMI, vol.37, no.3, pp. 583-596, 2015. DOI:10.1109/TPAMI.2014.2345390
  28. Galoogahi, H. K., Sim, T., Lucey, S, "Multichannel correlation filters," ICCV, pp. 4321-4328, 2013. DOI:10.1109/ICCV.2013.381