Skip to main content
SpringerLink
Log in

Implementation of a Moving Target Tracking Algorithm Using Eye-RIS Vision System on a Mobile Robot

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

A moving target tracking algorithm is proposed here and implemented on the Anafocus Eye-RIS vision system, which is a compact and modular platform to develop real time image processing applications. The algorithm combines moving-object detection with feature extraction in order to identify the specific target in the environment. The algorithm was tested in a mobile robotics experiment in which a robot, with the Eye-RIS mounted on it, pursued another one representing the moving target, demonstrating its performance and capabilities.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

References

  1. Acharya, T., & Ray, A. K. (2005). Image processing: Principles and applications. Wiley-Interscience.

  2. Cucchiara, R., Grana, C., Neri, G., Piccardi, M., & Pratim, A. (2001). The Sakbot system for moving object detection and tracking. Video-Based Surveillance Systems—Computer Vision and Distributed Processing, 145–157.

  3. Rota, N., & Thonnat, M. (2000). Video sequence interpretation for visual surveillance. Proc. of Third IEEE International Workshop on Visual Surveillance, 59–68.

  4. Jung, B., & Sukhatme, G. S. (2004). Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In The 8th Conference on Intelligent Autonomous Systems, 980–987, Amsterdam, The Netherlands.

  5. Behrad, A., Shahrokni, A., & Motamedi, S. A. (2001). A robust vision-based moving target detection and tracking system. In The Proceeding of Image and Vision Computing Conference, University of Otago, Dunedin, New Zealand.

  6. Lozano, O. M., & Otsuka, K. (2008). Real-time visual tracker by stream processing. Journal of Signal Processing Systems, 57(2), 285–295.

    Article  Google Scholar 

  7. Marinus, B., Leeuwen, V., & Groen, F. C. A. (2002). Motion interpretation for in-car vision systems. In The Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Lausanne, Switzerland.

  8. Yilmaz, A., Shafique, K., Lobo, N., Li, X., Olson, T., & Shah, M. A. (2001). Target-tracking in FLIR imagery using mean-shift and global motion compensation. In Workshop on Computer Vision Beyond the Visible Spectrum, Kauai, Hawaii.

  9. Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: a survey. ACM Computing Surveys, 38(4), Article 13.

  10. Rodríguez-Vázquez, A., Domínguez-Castro, R., Jiménez-Garrido, F., Morillas, S., Listán, J., Alba, L., et al. (2007). The Eye-RIS CMOS vision system. In Analog circuit design (pp. 15–32). Springer.

  11. Roska, T., & Rodriguez-Vazquez, A. (2002). Towards visual microprocessors. Proceedings of the IEEE, 90(7), 1244–1257.

    Article  Google Scholar 

  12. Karabiber, F., Arena, P., De Fiore, S., Vagliasindi, G., & Arik, S. (2009). A new segmentation algorithm implemented on the Eye-RIS Cmos vision system. Microtechnologies for the New Millennium in Bioengineered and Bioinspired Systems, Dresden Germany.

  13. Alba, L., Arena, P., De Fiore, S., & Patané, L. (2009). Robotic platforms and experiments. In Spatial temporal patterns for action-oriented perception in roving robots (pp. 399–422), Springer.

  14. Arena, P., Fortuna, L., Frasca, M., & Patanè, L., & Pollino, M. (2006). An autonomous mini-hexapod robot controlled through a CNN-based CPG VLSI chip. Proc. of CNNA, Istanbul.

  15. Serra, J. (1982). Image analysis and mathematical morphology. London: Academic Press.

    MATH  Google Scholar 

  16. http://www.analogic-computers.com/Support/Documentation/.

Download references

Acknowledgment

F.Karabiber was supported by the Scientific and Technical Research Council of Turkey under the Fellowship Program for Phd students. The work at the University of Catania was supported by the EU FP7 project SPARK II.

Author information

Authors and Affiliations

  1. Computer Engineering Department, Istanbul University, Istanbul, Turkey

    Fethullah Karabiber & Sabri Arik

  2. Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Università degli Studi di Catania, Catania, Italy

    Paolo Arena, Luigi Fortuna, Sabestiano De Fiore & Guido Vagliasindi

Authors
  1. Fethullah Karabiber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Arena
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luigi Fortuna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sabestiano De Fiore
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guido Vagliasindi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sabri Arik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fethullah Karabiber.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Karabiber, F., Arena, P., Fortuna, L. et al. Implementation of a Moving Target Tracking Algorithm Using Eye-RIS Vision System on a Mobile Robot. J Sign Process Syst 64, 447–455 (2011). https://doi.org/10.1007/s11265-010-0504-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-010-0504-7

Keywords

Search

Navigation