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Abrupt-motion-aware lightweight visual tracking for unmanned aerial vehicles

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Abstract

Visual tracking for unmanned aerial vehicles (UAVs) is a hot research topic for the wide applications of UAVs. As UAVs are high-altitude and high-freedom platforms, small targets in UAV tracking sequences are often under the attribute of large-scale location change due to the abrupt motion of the platform. Currently, many visual tracking methods based on the local search hypothesis have been widely researched on low-speed moving platforms. However, these methods cannot be directly used on the UAV platform, because targets will appear in any position of the new frame. To address this problem, we propose an abrupt-motion-aware visual tracking method in this paper. Because of the high power consumption of deep learning models, the proposed method is a lightweight tracker for small UAVs without the deep learning framework. Our method consists of three major components: abrupt motion estimation, object tracking and model updating. Abrupt motion often leads to abnormal changes in the response map of trackers. Thus, by analyzing the changes of tracking response maps, the abrupt motion can be detected efficiently. When abrupt motion happens, keypoint matching will be adaptively implemented to estimate the ego-motion and skipped otherwise. Then, the target location is predicted by the correlation filter tracker in a local search region. Moreover, according to the confidence analysis, an adaptive model update strategy is designed to alleviate the model noise caused by the short-term occlusion. Experimental results confirm the robustness and the accuracy of our method on challenging sequences and show the comparative performance of the proposed method against several state-of-the-art lightweight methods.

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Notes

  1. The proposed method focuses on visual tracking under the attribute of the abrupt motion, where two adjacent frames have enough point pairs for image registration.

References

  1. Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460–466 (2015)

    Article  Google Scholar 

  2. 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 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Belagiannis, V., Schubert, F., Navab, N., Ilic, S.: Segmentation based particle filtering for real-time 2d object tracking. Comput. Vis. ECCV 2012, 842–855 (2012)

    Google Scholar 

  5. Liu, T., Wang, G., Wang, L., Chan, K.L.: Visual tracking via temporally smooth sparse coding. IEEE Signal Process. Lett. 22(9), 1452–1456 (2015)

    Article  Google Scholar 

  6. Xing, J., Gao, J., Li, B., Hu, W., Yan, S.: Robust object tracking with online multi-lifespan dictionary learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 665–672 (2013)

  7. Lin, S.D., Lin, J.-J., Chuang, C.-Y.: Particle filter with occlusion handling for visual tracking. IET Image Process. 9(11), 959–968 (2015)

    Article  Google Scholar 

  8. 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 Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)

    Article  Google Scholar 

  9. Cheng, M., Zhang, Z., Lin, W.Y., Philip, T.: Bing: Binarized normed gradients for objectness estimation at 300fps. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3286–3293 (2014)

  10. Zitnick, C.L., Dollár, P.: Edge boxes: Locating object proposals from edges. 8693, 391–405 (2014)

  11. Fang, Z., Cao, Z., Xiao, Y., Zhu, L., Yuan, J.: Adobe boxes: locating object proposals using object adobes. IEEE Trans. Image Process. 25(9), 4116–4128 (2016). https://doi.org/10.1109/TIP.2016.2579311. ISSN 1057-7149

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhu, G., Porikli, F., Li, H.: Beyond local search: tracking objects everywhere with instance-specific proposals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 943–951 (2016)

  13. Liang, P., Pang, Y., Liao, C., Mei, X., Ling, H.: Adaptive objectness for object tracking. IEEE Signal Process. Lett. 23(7), 949–953 (2016)

    Article  Google Scholar 

  14. Huang, D.: Enable scale and aspect ratio adaptability in visual tracking with detection proposals. In: British Machine Vision Conference (2015)

  15. Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)

    Article  Google Scholar 

  16. Ma, C., Yang, X., Zhang, C., Yang, M.-H.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396, (2015)

  17. Ma, C., Yang, X., Zhang, C., Yang, M.-H.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)

  18. 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)

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

  20. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715. Springer (2012)

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

    Article  Google Scholar 

  22. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1430–1438 (2016)

  23. Zitnick, C. L., Dollár, P.: Edge boxes: locating object proposals from edges. In: ECCV (2014)

  24. Zhang, K., Li, X.Z., Zhang, J.X.: A robust point-matching algorithm for remote sensing image registration. IEEE Geosci. Remote Sens. Lett. 11(2), 469–473 (2014)

    Article  MathSciNet  Google Scholar 

  25. Wu, Y., Ma, W., Gong, M., Su, L., Jiao, L.: A novel point-matching algorithm based on fast sample consensus for image registration. IEEE Geosci. Remote Sens. Lett. 12(1), 43–47 (2017)

    Article  Google Scholar 

  26. Fang, Z., Cao, Z., Xiao, Y.: Structured output tracking guided by keypoint matching. In: Electro-Optical Remote Sensing X, volume 9988, page 99880Y. International Society for Optics and Photonics (2016)

  27. Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in neural information processing systems, pp. 529–536 (2005)

  28. Zhang, J., Ma, S., Sclaroff, S.: Meem: robust tracking via multiple experts using entropy minimization. In: European Conference on Computer Vision, pp. 188–203. Springer (2014)

  29. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  30. Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)

    Article  Google Scholar 

  31. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  32. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  33. Bian, J., Lin, W.-Y., Matsushita, Y., Yeung, S.-K., Nguyen, T.-D., Cheng, M.-M.: Gms: grid-based motion statistics for fast, ultra-robust feature correspondence. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)

  34. Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. arXiv preprint arXiv:1703.05020 (2017)

  35. Hare, S., Saffari, A., Torr, P.H.S : Efficient online structured output learning for keypoint-based object tracking. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1894–1901. IEEE (2012)

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (Grant No. 61702182) and the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3254).

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Authors and Affiliations

  1. National Key Lab of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, China

    Kaicheng Gong, Zhiguo Cao & Yang Xiao

  2. School of Energy and Mechanical-electronic Engineering, Hunan University of Humanities, Science and Technology, Loudi, China

    Zhiwen Fang

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  1. Kaicheng Gong
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  2. Zhiguo Cao
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Correspondence to Zhiguo Cao.

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Gong, K., Cao, Z., Xiao, Y. et al. Abrupt-motion-aware lightweight visual tracking for unmanned aerial vehicles. Vis Comput 37, 371–383 (2021). https://doi.org/10.1007/s00371-020-01805-9

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