Skip to main content
SpringerLink
Log in

Robust tracking of multiple persons in real-time video

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we present a robust person tracking method that the particle swarm optimization (PSO) algorithm is used as the tracking strategy. The method is divided into two procedures: object/background segmentation and tracking. For object/background segmentation, we use the temporal differencing to detect the regions of interest. For tracking, the PSO algorithm is used for overcome the robustness problem in the high noisy background and multiple moving persons and/or under occlusion. The particles in PSO represent the position, width and height of the search window, and the fitness function is calculated by the distance of the color feature vector and the histogram intersection. When occluded, we add the motion vector plus the previous position of the tracking model. The particles fly around the search region to obtain an optimal match of the target. The experiments show that the proposed method can track the single person, multiple people even when occluded, and is more efficient and accurate than the conventional particle filter method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Chan KL (2015) Detection of foreground in dynamic scene via two-step background subtraction. Mach Vis Appl 26:723–740

    Article  Google Scholar 

  2. Ching-Han C, Miao-Chun Y (2011) PSO-based multiple people tracking. In: Cherifi H, Zain J, El-Qawasmeh E (eds) Digital information and communication technology and its applications, vol 166. Springer, Berlin Heidelberg, pp 267–276

    Chapter  Google Scholar 

  3. Cuevas EV, Zaldivar D, Rojas R (2005) Kalman filter for vision tracking. Freie Univ., Fachbereich Mathematik und Informatik, Technical Report

  4. Djaghloul H, Batouche M, Jessel J-P (2010) Automatic PSO-based deformable structures markerless tracking in laparoscopic cholecystectomy. In: Graña Romay M, Corchado E, Garcia Sebastian MT (eds) Hybrid artificial intelligence systems, vol 6076. Springer Berlin, Heidelberg, pp 48–55

    Chapter  Google Scholar 

  5. Hayashi Y, Fujiyoshi H (2008) Mean-shift-based color tracking in illuminance change. In: Visser U, Ribeiro F, Ohashi T, Dellaert F (eds) RoboCup 2007: robot soccer world cup XI, vol 5001. Springer Berlin, Heidelberg, pp 302–311

    Chapter  Google Scholar 

  6. Hu M, Hu W, Tan T (2004) Tracking people through occlusions. ICPR’04 2:724–727

    Google Scholar 

  7. Jalal A, Singh V (2011) A robust background subtraction approach based on daubechies complex wavelet transform. In: Abraham A, Lloret Mauri J, Buford J, Suzuki J, Thampi S (eds) Advances in computing and communications, vol 191. Springer Berlin, Heidelberg, pp 516–524

    Chapter  Google Scholar 

  8. Liu YWWZJ, and Liu XTP (2008) A novel particle filter based people tracking method through occlusion. Proceedings of the 11th Joint Conference on Information Sciences

  9. Ornelas-Tellez F, Graff M, Sanchez E and Alanis A (2014) PSO optimal tracking control for state-dependent coefficient nonlinear systems. In: M. Jamshidi, V. Kreinovich, and J. Kacprzyk, (Eds.) Advance Trends in Soft Computing, vol. 312. Springer International Publishing, pp 403–410

  10. Pan I, Das S (2013) Design of hybrid regrouping PSO–GA based sub-optimal networked control system with random packet losses. Memet Comput 5:141–153

    Article  Google Scholar 

  11. Rymut B, Kwolek B (2014) Real-time multiview human body tracking using GPU-accelerated PSO. In: Wyrzykowski R, Dongarra J, Karczewski K, Waśniewski J (eds) Parallel processing and applied mathematics, vol 8384. Springer Berlin, Heidelberg, pp 458–468

    Chapter  Google Scholar 

  12. Sulistijono IAaNK (2007) Evolutionary robot vision and particle swarm intelligence robot vision for multiple human tracking of a partner robot. CEC 2007: 1535–1541

  13. Sulistijono IA and Kubota N (2007) Particle swarm intelligence robot vision for multiple human tracking of a partner robot. SICE Annual Conference 2007 pp 604–609

  14. Sulistijono IA, Kubota N (2007) Human head tracking based on particle swarm optimization and genetic algorithm. J Adv Comput Intell Intell Inform 11(6):681–687

    Google Scholar 

Download references

Acknowledgements

The authors are also grateful for the funding supported by the Ministry of Science and Technology of Taiwan (R.O.C.) under Grant No. NSC 103-2220-E-008 -003.

Author information

Authors and Affiliations

  1. Department of CSIE, National Central University, 320, Taoyuan, Taiwan, Republic of China

    Ching-Han Chen, Chien-Chun Wang & Miao-Chun Yan

Authors
  1. Ching-Han Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chien-Chun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miao-Chun Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ching-Han Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, CH., Wang, CC. & Yan, MC. Robust tracking of multiple persons in real-time video. Multimed Tools Appl 75, 16683–16697 (2016). https://doi.org/10.1007/s11042-016-3890-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3890-4

Keywords

Search

Navigation