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Application of gcForest to visual tracking using UAV image sequences

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Abstract

Visual object tracking is a core technology and challenging research in computer vision. With the rapid development of unmanned aerial vehicle (UAV), more and more UAVs are equipped with video cameras to conduct object tracking. Researching object tracking of UAV image sequences is of great help for practical applications. Recent trackers based on deep networks have a great success by learning an effective representation of targets, however, the offline training is time-consuming, and the complex network may reduce its efficiency. In this paper, to effectively decrease the training time and handle the problem of network complexity, an effective tracking algorithm called multi-scale gcForest tracking (MSGCF) is proposed. First, to enrich the sample information and overcome the problem of scale variations, the multi-scale transformation is used for helping network to obtain the stronger representation of target samples. Then, gcForest that is a decision tree ensemble approach is utilized in tracking task to extract the target features, and gcForest is much easier to train and works well in small-scale training data. Most importantly, we make improvements to the gcForest by adding three channels at the top of network to adapt to multi-scale images and improve the representation ability of network. Furthermore, computation speed can be increased greatly through the method of reducing feature dimension based on compressive sensing (CS) theory, and the power of features can also be reserved. Finally, a support vector machine (SVM) classifier is employed to effectively separate targets and backgrounds. Extensive experimental results on UAV image sequences and challenging benchmark data sets demonstrate that the proposed MSGCF algorithm is effective.

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References

  1. Achlioptas D (2003) Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J Comput Syst Sci 66(4):671–687

    Article  MathSciNet  MATH  Google Scholar 

  2. Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PH (2016) Staple: Complementary learners for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1401–1409

  3. Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In European conference on computer vision (pp. 850–865). Springer, Cham

  4. Bhat G, Johnander J, Danelljan M, Shahbaz Khan F, Felsberg M (2018) Unveiling the power of deep tracking. In: Proceedings of the European Conference on Computer Vision (ECCV) (pp. 483–498)

  5. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2544–2550). IEEE

  6. Chen T, Sahli H, Zhang Y, Yang T (2018) Improved compressive tracking based on pixelwise learner. Journal of Electronic Imaging 27(1):013003

    Article  Google Scholar 

  7. Choi J, Jin Chang H, Yun S, Fischer T, Demiris Y, Young Choi J (2017) Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4807–4816)

  8. Danelljan M, Bhat G, Shahbaz Khan F, Felsberg M (2017) Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6638–6646)

  9. Danelljan M, Häger G, Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In British Machine Vision Conference. BMVA Press, Nottingham

  10. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 58–66)

  11. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp. 4310–4318

  12. 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 (pp. 472–488). Springer, Cham

  13. Dinh TB, Vo N, Medioni G (2011) Context tracker: Exploring supporters and distracters in unconstrained environments. In: CVPR 2011 (pp. 1177–1184). IEEE

  14. Fan H, Ling H (2017) Sanet: Structure-aware network for visual tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 42–49

  15. Guo Q, Feng W, Zhou C, Huang R, Wan L, Wang S (2017) Learning dynamic siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1763–1771

  16. Hare S, Golodetz S, Saffari A, Vineet V, Cheng MM, Hicks SL, Torr PH (2016) Struck: Structured output tracking with kernels. IEEE Trans Pattern Anal Mach Intell 38(10):2096–2109

    Article  Google Scholar 

  17. He A, Luo C, Tian X, Zeng W (2018) A twofold siamese network for real-time object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4834–4843

  18. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision (pp. 702–715). Springer, Berlin, Heidelberg

  19. Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  20. 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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 749–758)

  21. Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network. In: International conference on machine learning (pp. 597–606)

  22. Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: 2012 IEEE Conference on computer vision and pattern recognition (pp. 1822–1829). IEEE

  23. Jiang W, Wang Y, Wang D (2018) Robust visual tracking using a contextual boosting approach. Journal of Electronic Imaging 27(2):023012

    Article  Google Scholar 

  24. Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: Bootstrapping binary classifiers by structural constraints. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 49–56). IEEE

  25. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422

    Article  Google Scholar 

  26. Kiani Galoogahi H, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (pp. 1135–1143)

  27. Li P, Wang D, Wang L, Lu H (2018) Deep visual tracking: Review and experimental comparison. Pattern Recogn 76:323–338

    Article  Google Scholar 

  28. Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: European conference on computer vision (pp. 254–265). Springer, Cham

  29. Liu N, Huo H, Fang T (2019) Robust object tracking via constrained online dictionary learning. Multimed Tools Appl 78(3):3689–3703

    Article  Google Scholar 

  30. Ma C, Huang JB, Yang X, Yang MH (2015) Hierarchical convolutional features for visual tracking. In Proceedings of the IEEE international conference on computer vision (pp. 3074–3082)

  31. Mueller M, Smith N, Ghanem B (2016) A benchmark and simulator for UAV tracking. In European conference on computer vision (pp. 445–461). Springer, Cham

  32. Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1396–1404)

  33. Nam H, Baek M, Han B (2016) Modeling and propagating cnns in a tree structure for visual tracking. arXiv preprint arXiv:1608.07242

  34. Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4293–4302)

  35. Ning J, Yang J, Jiang S, Zhang L, Yang MH (2016) Object tracking via dual linear structured SVM and explicit feature map. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4266–4274

  36. Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang MH (2016) Hedged deep tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4303–4311

  37. Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141

    Article  Google Scholar 

  38. Schuck PW (2005) Local correlation tracking and the magnetic induction equation. The Astrophysical Journal Letters 632(1):L53

    Article  Google Scholar 

  39. Song Y, Ma C, Wu X, Gong L, Bao L, Zuo W, Yang MH (2018) Vital: Visual tracking via adversarial learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8990–8999

  40. Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Torr PH (2017) 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)

  41. Wang H, Liu P, Du Y, Liu X (2019) Online convolution network tracking via spatio-temporal context. Multimed Tools Appl 78(1):257–270

    Article  Google Scholar 

  42. Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp. 3119–3127)

  43. Wu Y, Lim J, Yang MH (2013) Online object tracking: A benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2411–2418)

  44. Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848

    Article  Google Scholar 

  45. Yang H, Zhong D, Liu C, Song K, Yin Z (2018) Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters. Journal of Electronic Imaging 27(2):023008

    Article  Google Scholar 

  46. Zhang J, Ma S, Sclaroff S (2014) MEEM: robust tracking via multiple experts using entropy minimization. In: European conference on computer vision (pp. 188–203). Springer, Cham

  47. Zhang T, Xu C, Yang MH (2017) Multi-task correlation particle filter for robust object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4335–4343)

  48. Zhang K, Zhang L, Yang MH (2012) Real-time compressive tracking. In: European conference on computer vision (pp. 864–877). Springer, Berlin, Heidelberg

  49. Zhong W, Lu H, Yang MH (2012) Robust object tracking via sparsity-based collaborative model. In: 2012 IEEE Conference on Computer vision and pattern recognition (pp. 1838–1845). IEEE

  50. Zhou ZH, Feng J (2017) Deep forest: Towards an alternative to deep neural networks. arXiv preprint arXiv:1702.08835

  51. Zhu Z, Wu W, Zou W, Yan J (2018) End-to-end flow correlation tracking with spatial-temporal attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 548–557

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61171119). The authors greatly appreciate the financial support.

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  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Fang Liu & Anzhe Yang

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  1. Fang Liu
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  2. Anzhe Yang
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Correspondence to Anzhe Yang.

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Liu, F., Yang, A. Application of gcForest to visual tracking using UAV image sequences. Multimed Tools Appl 78, 27933–27956 (2019). https://doi.org/10.1007/s11042-019-07864-y

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  • DOI: https://doi.org/10.1007/s11042-019-07864-y

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