Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1469–1476 | Cite as

Human tracking by using multiple methods and weighted products

  • Fitri UtaminingrumEmail author
  • Winda Cahyaningrum
  • Randy Cahya Wihandika
  • Sigit Adinugroho
  • Mochammad Ali Fauzi
  • Yuita Arum Sari
  • Putra Pandu Adikara
  • Dahnial Syauqy
Original Paper


Conventional wheelchairs are used by people who can actuate their hand, but there are conditions that cause the user cannot operate the wheelchair, such as handicapped people and amputated arms. Hence, they need an assistant to operate the wheelchair, as we know those conditions are dependent on others. An intelligent wheelchair becomes a solution to this problem by tracking and following an assistant in front of a wheelchair; it uses a camera that has been embedded there. So the assistant does not need to move it. In order to track a human, an algorithm that can detect a human or object is needed. Some algorithms with different approaches have been proposed such as SIFT, SURF, BRISK, AKAZE, KAZE, and ORB. Each algorithm has its own excess and feebleness. This research devises multiple methods for a human tracking for smart wheelchair, which aims to cover the method drawbacks with the advantages of other methods. The proposed method is performed using multiple methods mentioned above, which have sorted by investigating the method score. Subsequently, the first method is used to track a human and yield keypoints. If the keypoints detected are less than the threshold value, then the next method is used and so on until it reaches the threshold value. The threshold value is obtained from the average of keypoints detected in the first frame. This devised method has been evaluated by using 6 videos recorded by us and 10 videos from Visual Tracker Benchmark. This proposed method achieved an average accuracy up to 0.848609 on 6 videos and 0.663 on OTB dataset.


Object detection Weighted product Keypoint-based Multiple methods 



  1. 1.
    Panchal, P., Prajapati, G., Patel, S., Shah, H., Nasriwala, J.: A review on object detection and tracking methods. Int. J. Res. Emerg. Sci. Technol. 2(1), 7–12 (2015)Google Scholar
  2. 2.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  4. 4.
    Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: Computer Vision (ICCV), pp. 2548–2555 (2011)Google Scholar
  5. 5.
    Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE features, vol. 7577, pp. 214–227 (2012)CrossRefGoogle Scholar
  6. 6.
    Alcantarilla, P. F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: British Machine Vision ConferenceGoogle Scholar
  7. 7.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision (2011)Google Scholar
  8. 8.
    Ha, S.-W., Moon, Y.-H.: Multiple object tracking using SIFT features and location matching. Int. J. Smart Home 5, 17–26 (2011)Google Scholar
  9. 9.
    Das, D., Saharia, S.: Implementation and performance evaluation of background subtraction algorithms. Int. J. Comput. Sci. Appl. (2014)CrossRefGoogle Scholar
  10. 10.
    Adikara, P.P., Wihandika, R.C., Utaminingrum, F., Sari, Y.A., Fauzi, M.A., Syauqy, D., Maulana, R.: Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair. Jurnal Ilmiah Teknologi Sistem Informasi 3, 11–16 (2017)CrossRefGoogle Scholar
  11. 11.
    Utaminingrum, F., Fitriyah, H., Wihandika, R.C., Fauzi, M.A., Syauqy, D., Maulana, R.: Fast obstacle distance estimation using laser line imaging technique for smart wheelchair. Int. J. Electr. Comput. Eng. 6, 1602 (2016)Google Scholar
  12. 12.
    Utaminingrum, F., Kurniawan, T. A., Fauzi, M. A., Maulana, R., Syauqy, D., Wihandika, R. C., Sari, Y. A., Adikara, P. P.: A Laser-Vision based obstacle detection and distance estimation for smart wheelchair navigation. In: International Conference in Signal and Image Processing, pp. 123–127 (2016)Google Scholar
  13. 13.
    Madbouly, A.M.M., Mostafa, M.-S.M., Wafy, M.: Performance assessment of feature detector-descriptor combination. Int. J. Comput. Sci. Issues 12(5), 87–94 (2015)Google Scholar
  14. 14.
    Yan, J., Wang, Z., Wang, S.: Real-time tracking of deformable objects based on combined matching-and-tracking. J. Electr. Imag. 25(2), 023011-1–023011-9 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D., Li, X.: A biologically inspired appearance model for robust visual tracking. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2357–2370 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lan, X., Ma, A.J., Yuen, P.C., Chellappa, R.: Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans. Image Process. 24(12), 5826 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Huang, C., Lucey, S., Ramanan, D.: Learning policies for adaptive tracking with deep feature cascades. In: Computer Vision Foundation (2017)Google Scholar
  18. 18.
    Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C., Zhou, H.: Point-to-Set distance metric learning on deep representations for visual tracking. IEEE Trans. Intell. Trans. Syst. 19, 187 (2017)CrossRefGoogle Scholar
  19. 19.
    Supreeth, H.S.G., Patil, C.M.: Efficient multiple moving object detection and tracking using combined background subtraction and clustering. Signal Image Video Process. 15, 1097 (2018)CrossRefGoogle Scholar
  20. 20.
    Asgarizadeh, M., Pourghassem, H.: A robust object tracking synthetic structure using regional mutual information and edge correlation-based tracking algorithm in aerial surveillance application. Signal Image Video Process 9, 175 (2013)CrossRefGoogle Scholar
  21. 21.
    Senna, P., Drummond, I. N., Bastos, G. S.: Real-time ensemble-based tracker with Kalman filter. In: 30th SIBGRAPI Conference on Graphics, Patterns and Images (2017)Google Scholar
  22. 22.
    Utaminingrum, F., Kurniawan, T. A., Fauzi, M. A., Wihandika, R. C., Adikra, P. P.: Adaptive human tracking for smart wheelchair. In: International Symposium on Computational and Business Intelligence (2017)Google Scholar

Copyright information

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

Authors and Affiliations

  • Fitri Utaminingrum
    • 1
    Email author
  • Winda Cahyaningrum
    • 1
  • Randy Cahya Wihandika
    • 1
  • Sigit Adinugroho
    • 1
  • Mochammad Ali Fauzi
    • 1
  • Yuita Arum Sari
    • 1
  • Putra Pandu Adikara
    • 1
  • Dahnial Syauqy
    • 1
  1. 1.Computer Vision Research Group, Faculty of Computer ScienceBrawijaya UniversityMalangIndonesia

Personalised recommendations