An RGBD Tracker Based on KCF Adaptively Handling Long-Term Occlusion

  • Xue-Fei Zhang
  • Ai-Ping Zeng
  • Shan Huang
  • Ming Qing
  • Yi ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


Since occlusion still be a challenge for object tracking in RGB data. In this paper, we propose an RGBD single-object tracker that built upon the well-known base KCF tracker and exploit how the depth information fusing to handle partial and long-term occlusion. To divides tracking model into parts, the proposed tracker could detect and handle occlusion of each part separately. Despite the robustness in tracking with long-term occlusion, our part-based tracker provides an adaptively updating learning matrix. Experimental results are conducted on our dataset, which demonstrate that our tracker contains stability in long-term tracking.


RGBD tracking Kernel Long-term occlusion RealSense 



At the point of finishing this paper, we are grateful for the support by the ChaoYing Technology, Co, Ltd, Sichuan, China.


  1. 1.
    Ma, C., Huang, J.B., Yang, X., et al.: Adaptive correlation filters with long-term and short-term memory for object tracking. Int. J. Comput. Vis. 1–26 (2017)Google Scholar
  2. 2.
    Bibi, A., Mueller, M., Ghanem, B.: Target response adaptation for correlation filter tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 419–433. Springer, Cham (2016). Scholar
  3. 3.
    Danelljan, M., Bhat, G., Khan, F.S., et al.: ECO: Efficient Convolution Operators for Tracking. pp. 6931–6939 (2016)Google Scholar
  4. 4.
    Henriques, João F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). Scholar
  5. 5.
    Henriques, J.F., Rui, C., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)CrossRefGoogle Scholar
  6. 6.
    Danelljan, M., Hager, G., Khan, F.S., et al.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)CrossRefGoogle Scholar
  7. 7.
    Dong, X., Shen, J., Wang, W., et al.: Occlusion-aware real-time object tracking by integrated circulant structure kernels classifier. IEEE Trans. Multimedia (2016)Google Scholar
  8. 8.
    Ma, C., Yang, X., Zhang, C., et al.: Long-term correlation tracking. In: Computer Vision and Pattern Recognition. pp. 5388–5396. IEEE (2015)Google Scholar
  9. 9.
    Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4902–4912. IEEE Computer Society (2015)Google Scholar
  10. 10.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  11. 11.
    Nummiaro, K., Koller-Meier, E., Gool, L.V.: An adaptive color-based particle filter. Image Vis. Comput. 21(1), 99–110 (2003)CrossRefGoogle Scholar
  12. 12.
    Song, S., Xiao, J.: Tracking revisited using RGBD camera: unified benchmark and baselines. In: IEEE International Conference on Computer Vision, pp. 233–240. IEEE (2014)Google Scholar
  13. 13.
    Hannuna, S., Camplani, M., Hall, J., et al.: DS-KCF: a real-time tracker for RGB-D data. J. Real-Time Image Process. 1–20 (2016)Google Scholar
  14. 14.
    Bibi, A., Zhang, T., Ghanem, B.: 3D part-based sparse tracker with automatic synchronization and registration. In: Computer Vision and Pattern Recognition, pp. 1439–1448. IEEE (2016)Google Scholar
  15. 15.
    Tang, F., Harville, M., Tao, H., et al.: Fusion of local appearance with stereo depth for object tracking. In: IEEE Computer Society Conference on, pp. 1–8. IEEE (2008)Google Scholar
  16. 16.
    Danelljan, M., Häger, G., Khan, F.S.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference. pp. 65.1–65.11 (2014)Google Scholar
  17. 17.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., et al.: Visual object tracking using adaptive correlation filters. In: Computer Vision and Pattern Recognition, pp. 2544–2550. IEEE (2010)Google Scholar
  18. 18.
    Tang, M., Feng, J.: Multi-kernel correlation filter for visual tracking. In: IEEE International Conference on Computer Vision, pp. 3038–3046. IEEE (2016)Google Scholar
  19. 19.
    Luber, M., Spinello, L., Kai, O.A.: People tracking in RGB-D data with on-line boosted target models. In: International Conference on Intelligent Robots and Systems, pp. 3844–3849. IEEE (2011)Google Scholar
  20. 20.
    Camplani, M., Paiement, A., Mirmehdi, M., et al.: Multiple Human Tracking in RGB-D Data: A Survey (2016)Google Scholar
  21. 21.
    Lim, H., Sinha, S.N.: Monocular localization of a moving person onboard a quadrotor MAV. In: IEEE International Conference on Robotics and Automation, pp. 2182–2189. IEEE (2015)Google Scholar
  22. 22.
    Dong, X., Shen, J., Yu, D., et al.: Occlusion-aware real-time object tracking. IEEE Trans. Multimedia 19(4), 763–771 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xue-Fei Zhang
    • 1
    • 2
  • Ai-Ping Zeng
    • 2
  • Shan Huang
    • 2
  • Ming Qing
    • 1
  • Yi Zhou
    • 1
    Email author
  1. 1.Chaoying Technology, Co., Ltd.ChengduChina
  2. 2.College of Electrical Engineering and Information TechnologySichuan UniversityChengduChina

Personalised recommendations