Occlusion detection via correlation filters for robust object tracking

Wei Zhang; Baosheng Kang

Occlusion detection via correlation filters for robust object tracking

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Author(s): Wei Zhang^{1, 2} and Baosheng Kang¹
- Affiliations: 1: School of Information Science and Technology , Northwest University , Xi'an 710127 , People's Republic of China ;
  2: Department of Computer Science , Baoji University of Arts and Sciences , Baoji 721016 , People's Republic of China
Source: Volume 2018, Issue 7, July 2018, p. 375 – 384
DOI: 10.1049/joe.2017.0795 , Online ISSN 2051-3305

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)

Received 16/10/2017, Accepted 12/04/2018, Revised 13/03/2018, Published 02/05/2018

This study presents a robust object tracking method based on occlusion detection via correlation filters. In the proposed method, multi-feature kernelised correlation filter is employed to estimate the preliminary location of the tracked target. To predict target occlusion state, the intrinsic relationship between the most reliable tracked target and its context information is exploited via correlation filters, together with a response stability constraint to make the detection more reliable. A long-term filter is activated to recover the target if the occlusion occurs. Furthermore, the model is updated adaptively based on the changes of occlusion state and target appearance to make the tracking process robust. Extensive experimental results demonstrate that the proposed tracking method with occlusion detection performs favourably against 15 state-of-the-art trackers over 100 challenge sequences on the object tracking benchmark OTB-2015.

References

1. 1)
  - 26. Kwon, J., Lee, K. M.: ‘Visual tracking decomposition’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, pp. 1269–1276.
2. 2)
  - 19. Zhu, G., Wang, J., Wu, Y.: ‘Collaborative correlation tracking’. Proc. British Machine Vision Conf., Swansea, UK, 2015, pp. 184.1–184.12.
3. 3)
  - 35. Babenko, B., Yang, M.H., Belongie, S.: ‘Robust object tracking with online multiple instance learning’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (8), pp. 1619–1632 (doi: 10.1109/TPAMI.2010.226).
4. 4)
  - 1. Bolme, D.S., Beveridge, J.R., Draper, B.A.: ‘Visual object tracking using adaptive correlation filters’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, pp. 2544–2550.
5. 5)
  - 24. Hare, S., Saffari, A., Torr, P.H.: ‘Struck: structured output tracking with kernels’. Proc. IEEE Conf. Computer Vision, Barcelona, Spain, 2011, pp. 263–270.
6. 6)
  - 18. Ma, C., Yang, X., Zhang, C.: ‘Long-term correlation tracking’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 5388–5396.
7. 7)
  - 8. Danelljan, M., Häger, G., Khan, F.: ‘Accurate scale estimation for robust visual tracking’. Proc. British Machine Vision Conf., Nottingham, 2014, pp. 65.1–65.11.
8. 8)
  - 6. Danelljan, M., Khan, F., Felsberg, S.M.: ‘Adaptive color attributes for real-time visual tracking’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1090–1097.
9. 9)
  - 11. Yang, M., Wu, Y., Hua, G.: ‘Context-aware visual tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 7, (31), pp. 1195–1209, doi: 10.1109/TPAMI.2008.146.
10. 10)
  - 21. Henriques, J., Carreira, J., Caseiro, R., et al: ‘Beyond hard negative mining: efficient detector learning via block-circulant decomposition’. Proc. IEEE Int. Conf. Computer Vision, Sydney, NSW, Australia, 2013, pp. 2760–2767.
11. 11)
  - 9. Zhang, K., Zhang, L., Liu, Q.: ‘Fast visual tracking via dense spatio-temporal context learning’. Proc. European Conf. Computer Vision, Zurich, 2014, pp. 127–141.
12. 12)
  - 17. Wu, Y., Lim, J., Yang, M.H.: ‘Object tracking benchmark’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 9, (37), pp. 1834–1848, (doi: 10.1109/TPAMI.2014.2388226).
13. 13)
  - 22. Grabner, H., Grabner, M., Bischof, H.: ‘Real-time tracking via on-line boosting’. Proc. British Machine Vision Conf., Edinburgh, UK, 2006, pp. 47–56.
14. 14)
  - 2. Henriques, J.F., Rui, C., Martins, P.: ‘Exploiting the circulant structure of tracking-by-detection with kernels’. Proc. European Conf. Computer Vision, Firenze, Italy, 2012, pp. 702–715.
15. 15)
  - 4. Khan, F.S., Anwer, R.M.: ‘Color attributes for object detection’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 3306–3313.
16. 16)
  - 16. Liu, T., Wang, G., Yang, Q.: ‘Real-time part-based visual tracking via adaptive correlation filters’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 4902–4912.
17. 17)
  - 13. Adam, A., Rivlin, E., Shimshoni, I.: ‘Robust fragments-based tracking using the integral histogram’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, New York, USA, 2006, pp. 798–805.
18. 18)
  - 5. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005, pp. 886–893.
19. 19)
  - 27. Zhong, W., Lu, H., Yang, M. H.: ‘Robust object tracking via sparsity-based collaborative model’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 1838–1845.
20. 20)
  - 15. Li, Y., Zhu, J., Hoi, S.C.H.: ‘Reliable patch trackers: robust visual tracking by exploiting reliable patches’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 353–361.
21. 21)
  - 14. Zhang, T., Jia, K., Xu, C., et al: ‘Partial occlusion handling for visual tracking via robust part matching’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1258–1265.
22. 22)
  - 9. Henriques, J.F., Caseiro, R., Martins, P., et al: ‘High-speed tracking with kernelized correlation filters’, TPAMI, 2015, 37, pp. 583–596 (doi: 10.1109/TPAMI.2014.2345390).
23. 23)
  - 7. Li, Y., Zhu, J.: ‘A scale adaptive kernel correlation filter tracker with feature integration’. Proc. European Conf. Computer Vision, Zurich, 2014, pp. 254–265.
24. 24)
  - 24. Kalal, Z., Mikolajczyk, K., Matas, J.: ‘Tracking-learning-detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (7), pp. 1409–1422 (doi: 10.1109/TPAMI.2011.239).
25. 25)
  - 28. Gao, J., Ling, H., Hu, W.: ‘Transfer learning based visual tracking with Gaussian processes regression’. Proc. European Conf. Computer Vision, Zurich, 2014, pp. 188–203.
26. 26)
  - 10. Dinh, T.B., Vo, N., Medioni, G.: ‘Context tracker: exploring supporters and distracters in unconstrained environments’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011, pp. 1177–1184.
27. 27)
  - 20. Wang, D., Lu, H., Xiao, Z.: ‘Inverse sparse tracker with a locally weighted distance metric’, IEEE Trans. Image Process., 2015, 9, (24), pp. 2646–2657, (doi: 10.1109/TIP.2015.2427518).
28. 28)
  - 12. Mueller, M., Smith, N., Ghanem, B.: ‘Context-aware correlation filter tracking’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017, pp. 1387–1395..

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