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Robust visual tracking via part-based model

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Abstract

In this paper, we propose a novel visual object tracking method using a part based appearance model. First, a local kernel feature is developed to encode edge information of patches. Next, bounding box of the target is divided into multiple parts. Then, each part uses correlation filter based tracking to predict position in the next frame. The matrix cosine similarity is utilized to measure reliabilities of the patches. Finally, optimal target location is predicted via maximizing the likelihood, which is obtained by adaptively fusing the reliable patches locations. Experimental results illustrate that our algorithm outperforms state-of-the-art tracking methods significantly in terms of accuracy and robustness.

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References

  1. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust L1 tracker using accelerated proximal gradient approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1830–1837 (2012)

  2. Hare, S., Saffari, A., Torr, P.: Struck: structured output tracking with kernel. In: ICCV, pp. 263–270 (2011)

  3. Cehovin, L., Kristan, M., Leonardis, A.: Robust visual tracking using an adaptive coupled-layer visual model. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 941–953 (2013)

    Article  Google Scholar 

  4. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1822–1829 (2012)

  5. Kwon, J., Lee, K.M.: Highly nonrigid object tracking via patch-based dynamic appearance modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2427–2441 (2013)

    Article  MathSciNet  Google Scholar 

  6. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 798–805 (2006)

  7. Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Partbased visual tracking with online latent structural learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)

  8. Cehovin, L., Kristan, M., Leonardis, A.: Robust visual tracking using an adaptive coupled-layer visual model. IEEE Trans. Pattern Anal. Mach. Intell. 35, 941–953 (2013)

    Article  Google Scholar 

  9. Chang, W., Chen, C., Hung, Y.: Tracking by parts: a bayesian approach with component collaboration. IEEE Trans. Syst. Man Cybern. Part B 39, 375–388 (2009)

    Article  Google Scholar 

  10. Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4902–4912 (2015)

  11. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 2544–2550 (2010)

  12. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)

    Article  Google Scholar 

  13. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: ECCV, pp. 254–265 (2014)

  14. Li, Y., Zhu, J., Hoi, S. C.: Reliable patch trackers: robust visual tracking by exploiting reliable patches, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 353–361 (2015)

  15. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)

  16. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)

    Article  Google Scholar 

  17. Collins, R.T., Liu, X., Lordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1631–1643 (2005)

    Article  Google Scholar 

  18. Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. IJCV 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  19. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: ECCV, pp. 864–877 (2012)

  20. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011)

    Article  Google Scholar 

  21. Hoey, J.: Tracking using flocks of features, with application to assisted handwashing. BMVC 1, 367–376 (2006)

    Google Scholar 

  22. Artner, N.M., Ion, A., Kropatsch, W.G.: Multi-scale 2d tracking of articulated objects using hierarchical spring systems. Pattern Recognit. 44(4), 800–810 (2011)

    Article  Google Scholar 

  23. Pernici, F., Del Bimbo, A.: Object tracking by oversampling local features. IEEE TAMI 36(12), 2538–2551 (2013)

    Google Scholar 

  24. Zhang, T., Changsheng, X., Yang, M.-H.: Robust structural sparse tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 473–486 (2019)

    Article  Google Scholar 

  25. Danelljan, M., Hager, G., Khan, F. S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC, pp. 1–11 (2014)

  26. Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: ECCV, pp. 127–141 (2014)

  27. Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 749–758 (2015)

  28. Zhang, T., Liu, S., Changsheng, X., Liu, B., Yang, M.-H.: Correlation particle filter for visual tracking. IEEE Trans. Image Process. (TIP) 27(6), 2676–2687 (2018)

    Article  MathSciNet  Google Scholar 

  29. Liu, T., Wang, G., Qingxiong, Y.: Real-time part-based visual tracking via adaptive correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

  30. Zhang, T., Changsheng, X., Yang, M.-H.: Learning multi-task correlation particle filters for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 365–378 (2019)

    Article  Google Scholar 

  31. Gao, J., Zhang, T., Yang, X., Changsheng, X.: P2t: Part-to-target tracking via deep regression learning. IEEE Trans. Image Process. 27(6), 3074–3086 (2018)

    Article  MathSciNet  Google Scholar 

  32. Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.S.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2805–2813 (2017)

  33. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: European conference on computer vision, pp. 850–865, Springer, Cham (2016)

  34. Wang, N., Shi, J., Yeung, D.-Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: ICCV (2015)

  35. Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)

    Article  Google Scholar 

  36. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVC (2006)

  37. Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: ECCV, pp. 234–247 (2008)

  38. Bai, Q., Wu, Z., Sclaro, S., Betke, M., Monnier, C.: Randomized ensemble tracking. In ICCV, pp. 2040–2047 (2013)

  39. Kwon, J., Lee, K.M.: Tracking by sampling and integrating multiple trackers. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1428–1441 (2014)

    Article  MathSciNet  Google Scholar 

  40. Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: ECCV, pp. 188–203, Cham (2014)

  41. Wang, N., Yeung, D.-Y.: Ensemble-based tracking: aggregating crowdsourced structured time series data. In: ICML, pp. 1107–1115 (2014)

  42. Seo, H., Milanfar, P.: Training-free, generic object detection using locally adaptive regression kernels. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1688–1704 (2010)

    Article  Google Scholar 

  43. Seo, H.J., Milanfar, P.: Face verification using the lark representation. IEEE Trans. Inf. Forensics Secur. 6(4), 1275–1286 (2011)

    Article  Google Scholar 

  44. Seo, H.J., Milanfar, P.: Action recognition from one example. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 867–882 (2011)

    Article  Google Scholar 

  45. Takeda, H., Milanfar, P., Protter, M., Elad, M.: Super-resolution without explicit subpixel motion estimation. IEEE Trans. Image Process. 18(9), 1958–1975 (2009)

    Article  MathSciNet  Google Scholar 

  46. Danelljan, M., Khan, F.S., Felsberg, M., Weijer, J.V.D.: Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090–1097 (2014)

  47. Fu, Y., Yan, S., Huang, T.S.: Correlation metric for generalized feature extraction. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2229–2235 (2008)

    Article  Google Scholar 

  48. Fu, Y., Huang, T.S.: Image classification using correlation tensor analysis. IEEE Trans. Image Process. 17(2), 226–234 (2008)

    Article  MathSciNet  Google Scholar 

  49. Chaudhuri, K., Freund, Y., Hsu, D.: A parameter-free hedging algorithm. In: NIPS (2009)

  50. Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., Ming-Hsuan Y.: Hedged deep tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4303–4311 (2016)

  51. Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. (2015)

  52. Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

  53. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N Learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2010)

  54. Kwon, J., Lee, K. M.: Visual tracking decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2010)

  55. Kwon, J., Lee, K. M.: Tracking by sampling trackers. In: ICCV (2011)

  56. Dinh, T.B., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2011)

  57. Henriques, F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV (2012)

  58. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

  59. Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

  60. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and K-selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2011)

  61. Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: ECCV (2002)

  62. Oron, S., Bar-Hillel, A., Levi, D., Avidan, S.: Locally orderless tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

  63. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)

    Article  Google Scholar 

  64. Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2012)

  65. Babenko, B., Yang, M.-H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)

    Article  Google Scholar 

  66. Wu, Y., Shen, B., Ling, H.: Online robust image alignment via iterative convex optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)

  67. Stalder, S., Grabner, H., van Gool, L.: Beyond semi-supervised tracking: tracking should be as simple as detection, but not simpler than recognition. In: ICCV Workshop (2009)

  68. Collins, R.: Mean-shift blob tracking through scale space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2003)

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China No. 61873246.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Yong Wang & Lu Ding

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Yong Wang

  3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinbin Luo & Shan Fu

  4. College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Huanlong Zhang

Authors
  1. Yong Wang
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  2. Xinbin Luo
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  3. Lu Ding
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  4. Shan Fu
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  5. Huanlong Zhang
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Correspondence to Xinbin Luo.

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Communicated by Q. Tian.

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Wang, Y., Luo, X., Ding, L. et al. Robust visual tracking via part-based model. Multimedia Systems 26, 607–620 (2020). https://doi.org/10.1007/s00530-020-00668-3

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