Adaptive sampling for UAV tracking

  • Yong Wang
  • Xinbin LuoEmail author
  • Lu Ding
  • Shan Fu
  • Shiqiang Hu
Original Article


Unmanned aerial vehicle (UAV)-based target tracking is a long-standing problem in UAV applications. In this paper, we develop a local kernel feature to encode properties of UAV tracking object. Meanwhile, object proposals can provide a reliable prior knowledge to identify tracking target being an object or not. Therefore, we propose to integrate detection proposal method into a tracking by detection framework. More specifically, we adopt edge box proposals and random samplings as training examples and then train these examples for tracking task. The structured support vector machine is employed to implement training and detecting procedure. To reveal the effectiveness of our method, experiment is performed on the UAV123 benchmark dataset. Among state-of-the-art methods, our method achieves comparable results.


UAV tracking Structured support vector machine Object proposals Edge boxes Local kernel feature 


Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.


  1. 1.
    Floreano D, Wood RJ (2015) Science, technology and the future of small autonomous drones. Nature 521(7553):460–466CrossRefGoogle Scholar
  2. 2.
    Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520–531CrossRefGoogle Scholar
  3. 3.
    Zhang H, Wang S, Xu X (2018) Tommy WS Chow, and QM Jonathan Wu, Tree2Vector: learning a vectorial representation for tree-structured data. In: IEEE transactions on neural networks and learning systemsGoogle Scholar
  4. 4.
    Henriques Joao F, Caseiro R, Martins P, Batista J (2015) High-speed tracking with Kernelized correlation filters. TPAMI. Google Scholar
  5. 5.
    Danelljan M, Hger G, Khan FS, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: BMVCGoogle Scholar
  6. 6.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. TPAMI 25(5):564–577CrossRefGoogle Scholar
  7. 7.
    Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. In: CVPR, pp 1822–1829Google Scholar
  8. 8.
    Liu B, Huang J, Yang L, Kulikowsk C (2011) Robust tracking using local sparse appearance model and k-selection. In: CVPR, pp 1313–1320Google Scholar
  9. 9.
    Zhong W, Lu H, Yang M-H (2012) Robust object tracking via sparsity-based collaborative model. In: CVPR, pp 1838–1845Google Scholar
  10. 10.
    Cheng M-M, Zhang Z, Lin W-Y, Torr P (2014) Bing: binarized normed gradients for objectness estimation at 300fps. In CVPRGoogle Scholar
  11. 11.
    Zitnick CL, Dollar P (2014) Edge boxes: locating object proposals from edges. In: ECCVGoogle Scholar
  12. 12.
    Smeulders AWM, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. TPAMI 36(7):1442–1468CrossRefGoogle Scholar
  13. 13.
    Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2411–2418Google Scholar
  14. 14.
    Haijun Z, Ho JKL, Wu JQM, Ye Y (2013) Multi-dimensional latent semantic analysis using term spatial information. IEEE Trans Syst Man Cybern B 43(6):1625–1640Google Scholar
  15. 15.
    Kalal Z, Mikolajczyk K, Matas J (2011) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422CrossRefGoogle Scholar
  16. 16.
    Ma C, Huang J-B, Yang X, Yang M-H (2015) Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 3074–3082Google Scholar
  17. 17.
    Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang M-H (2016) Hedged deep tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4303–4311Google Scholar
  18. 18.
    Danelljan M, Hager G, Khan FS, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: Proceedings of the IEEE international conference on computer vision workshops, pp 58–66Google Scholar
  19. 19.
    Danelljan M, Robinson A, Khan FS, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. In European conference on computer vision. Springer, Cham, pp 472–488Google Scholar
  20. 20.
    Ross D, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141CrossRefGoogle Scholar
  21. 21.
    Liang P, Pang Y, Liao C, Mei X, Ling H (2016) Adaptive objectness for object tracking. IEEE Signal Process Lett 23(7):949–953CrossRefGoogle Scholar
  22. 22.
    Huang D, Luo L, Wen M, Chen Z, Zhang C (2015) Enable scale and aspect ratio adaptability in visual tracking with detection proposals. In: Xie X, Jones MW, Tam GKL (eds) Proceedings of the British machine vision conference (BMVC). BMVA Press, pp 185.1–185.12Google Scholar
  23. 23.
    Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271CrossRefGoogle Scholar
  24. 24.
    Grabner H, Bischof H (2006) On-line boosting and vision. In: IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 260–267Google Scholar
  25. 25.
    Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: ECCV. Springer, pp 234–247Google Scholar
  26. 26.
    Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. In: ICCVGoogle Scholar
  27. 27.
    Zhang J, Ma S, Sclaroff S (2014) Meem: robust tracking via multiple experts using entropy minimization. In: ECCV. Springer, pp 188–203Google Scholar
  28. 28.
    Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: CVPR, pp 2544–2550Google Scholar
  29. 29.
    Danelljan M, Khan FS, Felsberg M, van de Weijer J (2014) Adaptive color attributes for real-time visual tracking. In: CVPR, pp 1090–1097Google Scholar
  30. 30.
    Ma C, Yang X, Zhang C, Yang M-H (2015) Long-term correlation tracking. In: CVPR, pp 5388–5396Google Scholar
  31. 31.
    Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. In: BMVA, pp 6.1–6.10Google Scholar
  32. 32.
    Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189–2202CrossRefGoogle Scholar
  33. 33.
    Chang K, Liu T, Chen H, Lai S (2011) Fusing generic objectness and visual saliency for salient object detection. In: ICCVGoogle Scholar
  34. 34.
    Mihir J, Van Gemert J, Jégou H, Bouthemy P, Snoek C (2014) Action localization with tubelets from motion. In: CVPRGoogle Scholar
  35. 35.
    Tao R, Gavves E, Snoek CG, Smeulders AW (2014) Locality in generic instance search from one example. In: CVPRGoogle Scholar
  36. 36.
    Juneja M, Vedaldi A, Jawahar C, Zisserman A (2013) Blocks that shout: distinctive parts for scene classification. In: CVPRGoogle Scholar
  37. 37.
    Uijlings JR, van de Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171CrossRefGoogle Scholar
  38. 38.
    Zhang Z, Warrell J, Torr PH (2011) Proposal generation for object detection using cascaded ranking SVMS. In: CVPRGoogle Scholar
  39. 39.
    Endres I, Hoiem D (2014) Category-independent object proposals with diverse ranking. IEEE Trans Pattern Anal Mach Intell 36(2):222–234CrossRefGoogle Scholar
  40. 40.
    Gao Z, Porikli F, Li H (2016) Beyond local search: tracking objects everywhere with instance-specific proposals. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 943–951Google Scholar
  41. 41.
    Wu Y, Lim J, Yang M-H (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848CrossRefGoogle Scholar
  42. 42.
    Matej K, Matas J, Leonardis A, Felsberg M, Cehovin L, Fernandez G, Vojir T, Hager G, Nebehay G, Pflugfelder R (2015) The visual object tracking vot2015 challenge results. In: Proceedings of the IEEE international conference on computer vision workshops, pp 1–23Google Scholar
  43. 43.
    Shuran S, Xiao J (2013) Tracking revisited using RGBD camera: Unified benchmark and baselines. In: Proceedings of the IEEE international conference on computer vision, pp 233–240Google Scholar
  44. 44.
    Li A, Lin M, Yi W, Yang M-H, Yan S (2016) Nus-pro: a new visual tracking challenge. IEEE Trans Pattern Anal Mach Intell 38(2):335–349CrossRefGoogle Scholar
  45. 45.
    Collins R, Zhou X, Teh SK (2005) An open source tracking testbed and evaluation web site. In: IEEE int’l workshop on performance evaluation of tracking and surveillance, pp 17–24Google Scholar
  46. 46.
    Matthias M, Smith N, Ghanem B (2016) A benchmark and simulator for UAV tracking. In: European conference on computer vision. Springer, pp 445–461Google Scholar
  47. 47.
    Elsey M, Esedoglu S (2009) Analogue of the total variation denoising model in the context of geometry processing. SIAM Multiscale Model Simul 7(4):1549–1573MathSciNetCrossRefzbMATHGoogle Scholar
  48. 48.
    Lee S-H, Seo JK (2005) Noise removal with Gauss curvature-driven diffusion. IEEE Trans Image Process 14(7):904–909MathSciNetCrossRefGoogle Scholar
  49. 49.
    Seo H, Milanfar P (2010) Training-free, generic object detection using locally adaptive regression kernels. IEEE Trans Pattern Anal Mach Intell 32(9):1688–1704CrossRefGoogle Scholar
  50. 50.
    Seo HJ, Milanfar P (2011) Face verification using the lark representation. IEEE Trans Inf Forensic Secur 6(4):1275–1286CrossRefGoogle Scholar
  51. 51.
    Takeda H, Farsiu S, Milanfar P (2007) Kernel regression for image processing and reconstruction. IEEE Trans Image Process 16(2):349–366MathSciNetCrossRefGoogle Scholar
  52. 52.
    Gao Z, Porikli F, Li H (2016) Not all negatives are equal: learning to track with multiple background clusters. IEEE Trans Circuits Syst Video Technol 28:314–326Google Scholar
  53. 53.
    Bordes A, Bottou L, Gallinari P, Weston J (2007) Solving multiclass support vector machines with larank. In: ICMLGoogle Scholar
  54. 54.
    Henriques J, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV, pp 702–715Google Scholar
  55. 55.
    Hong Z, Chen Z, Wang C, Mei X, Prokhorov D, Tao D (2015) Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: IEEE conference on CVPR, pp 749–758Google Scholar
  56. 56.
    Martin D, Hager G, Khan FS, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp 4310–4318Google Scholar
  57. 57.
    Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: 2014 workshops computer vision-ECCV, pp 254–265Google Scholar
  58. 58.
    Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: CVPR, pp 2544–550Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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