Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Robust UAV-based tracking using hybrid classifiers

  • Original paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Visual object tracking is a key factor for unmanned aerial vehicles. In this paper, we propose a robust and effective visual object tracking method with an appearance model based on the locally adaptive regression kernel. The proposed appearance model encodes the geometric structure of the target. The tracking problem is formulated as two binary classifiers via two support vector machines (SVMs) with online model update. The backward tracking which tracks the target in reverse of time is utilized to evaluate the accuracy and robustness of the two SVMs. The final locations are adaptively fused based on the results of the forward tracking and backward tracking validation. Several state-of-the-art tracking algorithms are evaluated on large-scale benchmark datasets which include challenging factors such as heavy occlusion, pose variation, illumination variation and motion blur. Experimental results demonstrate that our method achieves appealing performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Wu, Yi, Lim, Jongwoo, Yang, Ming-Hsuan: Object tracking benchmark. IEEE TPAMI 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  2. Mondragon, I.F., Campoy, P., Olivares-Mendez, M.A., Martinez, C.: 3D object following based on visual information for unmanned aerial vehicles. In: IX Latin American Robotics Symposium and IEEE Colombian Conference on Automatic Control, pp. 1–7 (2011)

  3. Teuliere, C., Eck, L., Marchand, E.: Chasing a moving target from a flying UAV. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, September, pp. 4929–4934 (2011)

  4. Kendall, A., Salvapantula, N., Stol, K.: On-board object tracking control of a quadcopter with monocular vision. In : International Conference on Unmanned Aircraft Systems, pp. 404–411 (2014)

  5. Cao, X., Wu, C., Lan, J., Yan, P., Li, X.: Vehicle detection and motion analysis in low-altitude airborne video under urban environment. IEEE Trans. Circuits Syst. Video Technol. 21(10), 1522–1533 (2011)

    Article  Google Scholar 

  6. Wainwright, A., Ford, J.: Fusion of morphological images for airborne target detection. In: 15th International Conference on Information Fusion, pp. 1180–1187 (2012)

  7. Gandhi, T., Yang, M.-T., Kasturi, R., Camps, O., Coraor, L., Mc-Candless, J.: Detection of obstacles in the flight path of an aircraft. IEEE CVPR 2, 304–311 (2000)

    Google Scholar 

  8. Carnie, R., Walker, R., Corke, P.: Image processing algorithms for UAV sense and avoid. In: ICRA, pp. 2848–2853 (2006)

  9. Lai, J.S., Ford, J.J., Mejias, L., OShea, P.J., Walker, R.A.: See and avoid using on-board computer vision. In: Angelov, P. (ed.) Sense and Avoid in UAS, Research and Applications, pp. 265–294. Wiley, Hoboken (2012)

    Chapter  Google Scholar 

  10. Dey, D., Geyer, C., Singh, S., Digioia, M.: Passive, long-range detection of aircraft: towards a field deployable sense and avoid system. In: Zelinsky, A. (ed.) Field and Service Robotics, Springer Tracts in Advanced Robotics Series, vol. 62, pp. 113–123. Springer, Berlin (2010)

    Google Scholar 

  11. Mccandless, J.W.: Detection of aircraft in video sequences using a predictive optical flow algorithm. Opt. Eng. 38, 523–530 (1999)

    Article  Google Scholar 

  12. Mian, A.: Realtime Visual Tracking of Aircrafts, Digital Image Computing: Techniques and Applications, pp. 351–356. Massey University, Auckland (2008)

    Google Scholar 

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

  14. 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: IEEE CVPR, pp. 749–758 (2015)

  15. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE TPAMI 34(7), 1409–1422 (2011)

    Article  Google Scholar 

  16. Wang, Y., Shi, W., Wu, S.: Robust UAV-based tracking using hybrid classifiers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 2129–2137 (2017)

  17. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)

    Article  Google Scholar 

  18. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE TPAMI 36(7), 1442–1468 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE CVPR, pp. 1822–1829 (2012)

  21. Gong, C., Fu, K., Loza, A., Wu, Q., Liu, J., Yang, J.: PageRank tracker: from ranking to tracking. IEEE Transactions on Cybernetics 44, 882–893 (2013)

    Article  Google Scholar 

  22. Liu, F., Gong, C., Zhou, T., Fu, K., Yang, J.: Visual tracking via nonnegative multiple coding. IEEE Trans. Multimed. 19, 2680–2691 (2017)

    Article  Google Scholar 

  23. Liu, F., Zhou, T., Gong, C., Fu, K., Bai, L., Yang, J.: Inverse nonnegative local coordinate factorization for visual tracking. IEEE Trans. Circuits Syst. Video Technol. 28, 1752–1764 (2017)

    Article  Google Scholar 

  24. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: BMVA, pp. 6.1–6.10 (2006)

  25. Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M.-M., Hicks, S.L., Torr, P.H.S.: Struck: structured output tracking with kernels. IEEE TPAMI 38(10), 2096–2109 (2016)

    Article  Google Scholar 

  26. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE CVPR, pp. 2544–2550 (2010)

  27. Henriques, J., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV, pp. 702–715 (2012)

  28. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE TPAMI 37, 583–596 (2015)

    Article  Google Scholar 

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

  30. van de Weijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: Learning color names for real-world applications. IEEE TIP 18(7), 1512–1524 (2009)

    MathSciNet  MATH  Google Scholar 

  31. Dalal, N., Triggs, B.: Histogram of oriented gradietns for human detection. In: IEEE CVPR, pp. 886–893 (2005)

  32. Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC (2014)

  33. Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: IEEE ICCV, pp. 4310–4318 (2015)

  34. Liu, F., Gong, C., Huang, X., Zhou, T., Yang, J., Tao, D.: Robust visual tracking revisited: from correlation filter to template matching. IEEE Trans. Image Process. 27(6), 2777–2790 (2018)

    Article  MathSciNet  Google Scholar 

  35. Fu, C., Carrio, A., Olivares-Mendez, M., Suarez-Fernandez, R., Campoy, P, Robust real-time vision-based aircraft tracking from unmanned aerial vehicles. In: IEEE ICRA, pp. 5441–5446 (2014)

  36. Nussberger, A., Grabner, H., Van Gool, L.: Aerial object tracking from an airborne platform. In: International Conference on Unmanned Aircraft Systems, pp. 1284–1293 (2014)

  37. Qadir, A., Neubert, J., Semke, W., Schultz, R.: Chap: On-Board Visual Tracking With Unmanned Aircraft System (UAS). Infotech@Aerospace Conferences, AIAA (2011)

  38. Pestana, J., Sanchez-Lopez, J., Campoy, P., Saripalli, S.: Vision based GPS-denied object tracking and following for unmanned aerial vehicles. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 1–6 (2013)

  39. Lim, H., Sinha, S.N.: Monocular localization of a moving person onboard a quadrotor MAV. In: IEEE ICRA, pp. 2182–2189 (2015)

  40. Mueller, M., Sharma, G., Smith, N., Ghanem, B.: Persistent aerial tracking system for UAVs. In: IEEE IROS, pp. 1562–1569 (2016)

  41. Cao, X., Jiang, X., Li, X., Yan, P.: Correlation-based tracking of multiple targets with hierarchical layered structure. IEEE Trans. Cybern. 48(1), 90–102 (2018)

    Article  Google Scholar 

  42. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: IEEE CVPR, pp. 2411–2418 (2013)

  43. Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., Cehovin, L., Nebehay, G., Fernandez, G., Vojir, T., Gatt, A., Khajenezhad, A.: The VOT2013 challenge results. In: CVPRW, pp. 98–111 (2013)

  44. Liang, Pengpeng, Blasch, Erik, Ling, Haibin: Encoding color information for visual tracking: algorithms and benchmark. IEEE TIP 24(12), 5630–5644 (2015)

    MathSciNet  Google Scholar 

  45. Collins, R., Zhou, X., The, S.K.: The PASCAL VOC2010 results (2010)

  46. Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: ECCV, pp. 445–461 (2016)

  47. Seo, H., Milanfar, P.: Training-free, generic object detection using locally adaptive regression kernels. IEEE TPAMI 32(9), 1688–1704 (2010)

    Article  Google Scholar 

  48. Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 1–27 (2009)

    Article  Google Scholar 

  49. Seo, H.J., Milanfar, P.: Action recognition from one example. IEEE TPAMI 33(5), 867–882 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2010 (VOC2010) results (2010)

  52. Dinh, T.B., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: CVPR (2011)

  53. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and K-selection. In: CVPR (2011)

  54. Babenko, Boris, Yang, Ming-Hsuan, Belongie, Serge: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)

    Article  Google Scholar 

  55. Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: CVPR (2012)

  56. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)

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

  58. Wang, Z., Vucetic, S.: Online training on a budget of support vector machines using twin prototypes. Stat. Anal. Data Min. 3(3), 149–169 (2010)

    MathSciNet  Google Scholar 

  59. Collins, R., Zhou, X., Teh, S.K.: An open source tracking testbed and evaluation web site. IEEE International Workshop on PETS, pp. 17–24 (2005)

Download references

Author information

Authors and Affiliations

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

    Yong Wang

  2. INSKY Lab, Leotail Intelligent Tech, 1628, Pudong District, Shanghai, China

    Wei Shi

  3. Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems, University of Pittsburgh, Pittsburgh, USA

    Shandong Wu

Authors
  1. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shandong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shandong Wu.

Additional information

This work was partially supported by a National Institutes of Health (NIH)/National Cancer Institute (NCI) R01 Grant (#1R01CA193603).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 3290 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Shi, W. & Wu, S. Robust UAV-based tracking using hybrid classifiers. Machine Vision and Applications 30, 125–137 (2019). https://doi.org/10.1007/s00138-018-0981-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0981-4

Keywords

Search

Navigation