Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Robust Visual Tracking using Search Area Estimation and Multi-channel Local Edge Pattern

  • Received : 2017.05.29
  • Accepted : 2017.07.23
  • Published : 2017.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

Keywords

References

  1. Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M. M., Hicks, S. L., & Torr, P. H., "Struck: Structured output tracking with kernels." IEEE transactions on pattern analysis and machine intelligence, 38(10), pp. 2096-2109, 2016. https://doi.org/10.1109/TPAMI.2015.2509974
  2. Danelljan, M., Shahbaz Khan, F., Felsberg, M., & Van de Weijer, J. "Adaptive color attributes for real-time visual tracking." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 1090-1097, 2014
  3. DANELLJAN, Martin, et al. "Discriminative scale space tracking." IEEE transactions on pattern analysis and machine intelligence, 2016.
  4. M. Kristan et al., "The visual object tracking VOT2014 challenge results," In ECCV Visual Object Tracking Challenge Workshop, 2014.
  5. Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., ... & Pflugfelder, R. "The visual object tracking vot2015 challenge results." In Proceedings of the IEEE international conference on computer vision workshops, pp. 1-23, June 2010.
  6. Kiani Galoogahi, H., Sim, T., & Lucey, S. "Multi-channel correlation filters." IIn Proceedings of the IEEE International Conference on Computer Vision , pp. 3072-3079, 2013.
  7. Danelljan, M., Häger, G., Khan, F., & Felsberg, M. "Accurate scale estimation for robust visual tracking." In British Machine Vision Conference, Nottingham, BMVA Press. September 1-5, 2014.
  8. Jabid, T., Kabir, M. H., &Chae, O. "Local directional pattern (LDP) for face recognition," In Consumer Electronics (ICCE), 2010 Digest of Technical Papers International Conference on, pp. 329-330, January 2015.
  9. Bolme, D. S., Beveridge, J. R., Draper, B. A., & Lui, Y. M. "Visual object tracking using adaptive correlation filters." In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2544-2550, June 2010.
  10. Baker, S., & Matthews, I. "Lucas-kanade 20 years on: A unifying framework." International journal of computer vision, pp. 221-255. 56(3), 2004. https://doi.org/10.1023/B:VISI.0000011205.11775.fd
  11. Yves Bouguet, Jean. "Pyramidal implementation of the lucas kanade feature tracker," Intel Corporation, Microprocessor Research Labs, 2000.