Skip to main content
SpringerLink
Log in

Achieving real-time object detection and tracking under extreme conditions

  • Survey Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

In this survey, we present a brief analysis of single camera object detection and tracking methods. We also give a comparison of their computational complexities. These methods are designed to accurately perform under difficult conditions such as erratic motion, drastic illumination change, and noise contamination.

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

Similar content being viewed by others

References

  1. Karman, K.-P., von Brandt, A.: Moving object recognition using an adaptive background memory. In: Capellini, V. (ed.) Time-varying Image Processing and Moving Object Recognition, vol. II. Elsevier, Amsterdam, pp 297–307 (1990)

  2. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pp. 255–261 (1999)

  3. Stauffer, C., Grimson, E.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, vol. II, pp. 246–252 (1999)

  4. Porikli, F., Tuzel, O.: Bayesian background modeling for foreground detection. In: Proceedings of the ACM Visual Surveillance and Sensor Network (2005)

  5. Javed, K., Shafique, O., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: IEEE Workshop on Motion and Video Computing (2002)

  6. Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Proceedings of the European Conference on Computer Vision, Dublin, Ireland, vol. II, pp. 751–767 (2000)

  7. Porikli, F., Wren, C.: Change detection by frequency decomposition: wave-back. In: Proceedings of Workshop on Image Analysis for Multimedia Interactive Services, Montreux (2005)

  8. Zhu, Q., Avidan, S., Cheng, K.: Learning a sparse, corner-based representation for time-varying background modelling. In: Proceedings of the 10th International Conference on Computer Vision, Beijing, China (2005)

  9. Porikli, F.: Multiplicative background–foreground estimation under uncontrolled illumination using intrinsic images. In: Proceedings of IEEE Motion Multi-Workshop, Breckenridge (2005)

  10. Wren, C., Azarbayejani, A., Darell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19, 780–785 (1997)

    Article  Google Scholar 

  11. Boykov, Y., Huttenlocher, D.: Adaptive bayesian recognition in tracking rigid objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, vol. II, pp 697–704 (2000)

  12. Isard, M., Blake, I.: Condensation—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29, 5–28 (1998)

    Article  Google Scholar 

  13. Bouaynaya, N., Qu, W., Schonfeld, D.: An online motion-based particle filter for head tracking applications. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia (2005)

  14. Zhou, S., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-based modeling in particle filters. In: Proceedings of the International Conference on Multimedia and Expo, Baltimore (2003)

  15. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, vol. 1, pp. 142–149 (2000)

  16. Porikli, F., Tuzel, O.: Object tracking in low-frame-rate video. In: Proceedings of PIE/EI—Image and Video Communication and Processing, San Jose, CA (2005)

  17. Avidan, S.: Ensemble tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA (2005)

  18. Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Proceedings of the 9th European Conference on Computer Vision, Graz, Austria (2006)

  19. Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on lie algebra. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY (2006)

  20. Porikli, F.: Integral histogram: a fast way to extract histograms in cartesian spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 1, pp. 829–836 (2005)

  21. McCurry, P., Morgan, F., Kilmartin, L.: Xilinx fpga implementation of a pixel processor for object detection applications. In: Proceedings of the International Conference on Image Processing, vol. 3, pp. 346–349 (2001)

Download references

Author information

Authors and Affiliations

  1. Merl Technology Lab, 201 Broadway, Cambridge, MA, USA

    Fatih Porikli

Authors
  1. Fatih Porikli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatih Porikli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Porikli, F. Achieving real-time object detection and tracking under extreme conditions. J Real-Time Image Proc 1, 33–40 (2006). https://doi.org/10.1007/s11554-006-0011-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-006-0011-z

Keywords

Search

Navigation