Skip to main content
SpringerLink
Log in

Real-time moving object detection algorithm on high-resolution videos using GPUs

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

Abstract

Modern imaging sensors with higher megapixel resolution and frame rates are being increasingly used for wide-area video surveillance (VS). This has produced an accelerated demand for high-performance implementation of VS algorithms for real-time processing of high-resolution videos. The emergence of multi-core architectures and graphics processing units (GPUs) provides energy and cost-efficient platform to meet the real-time processing needs by extracting data level parallelism in such algorithms. However, the potential benefits of these architectures can only be realized by developing fine-grained parallelization strategies and algorithm innovation. This paper describes parallel implementation of video object detection algorithms like Gaussians mixture model (GMM) for background modelling, morphological operations for post-processing and connected component labelling (CCL) for blob labelling. Novel parallelization strategies and fine-grained optimization techniques are described for fully exploiting the computational capacity of CUDA cores on GPUs. Experimental results show parallel GPU implementation achieves significant speedups of ~250× for binary morphology, ~15× for GMM and ~2× for CCL when compared to sequential implementation running on Intel Xeon processor, resulting in processing of 22.3 frames per second for HD videos.

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.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Kumar, P., Roy, S., Mittal, A., Kumar, P.: OS-Guard: a novel framework for multimedia surveillance data management. J Multimed Technol Appl 59(2), 363–382 (2012)

    Article  Google Scholar 

  2. Jefferson, K., Lee, C.: Computer vision workload analysis—case study of video surveillance systems. Intel Technol. J. 9(2), 109–118 (2005)

    Google Scholar 

  3. Manohar, M., Ramapriyan, H.K.: Connected component labeling of binary images on a mesh connected massively parallel processor. Comput. Vis. Graph. Image Process. 45(2), 133–149 (1989)

    Article  Google Scholar 

  4. Boyer, M., Tarjan, D., Acton, S.T., Skadron, K.: Accelerating leukocyte tracking using CUDA: a case study in leveraging manycore coprocessors (2009)

  5. Pavlidis, I., Morellas, V., Tsiamyrtzis, P., Harp, S.: Urban surveillance systems: from the laboratory to the commercial world. Proc IEEE 89(10), 1478–1497 (2001)

    Article  Google Scholar 

  6. Collins, R.T., Lipton, A. J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: VSAM: a system for video surveillance and monitoring. Technical Report CMU-RI-TR-00-12, Carnegie Mellon University, Pittsburgh, PA (2000)

  7. Sankaranarayanan, A.C., Veeraraghavan, A., Chellappa, R.: Object detection, tracking and recognition for multiple smart cameras. Proc. IEEE 96(10), 1606–1624 (2008)

    Google Scholar 

  8. Kolekar, M.H., Palaniappan, K., Sengupta, S., Seetharaman, G.: Semantic concept mining based on hierarchical event detection for soccer video indexing. J. Multimed. 4(5), 298–312 (2009)

    Google Scholar 

  9. Bibby, C., Reid, I.D.: Robust real-time visual tracking using pixelwise posteriors. In: European Conference on Computer Vision, pp. II:831–II:844 (2008)

  10. Shyu, C.R., Klaric, M., Scott, G., Barb, A., Davis, C., Palaniappan, K.: GeoIRIS: geospatial information retrieval and indexing system—content mining, semantics, modeling, and complex queries. IEEE Trans. Geosci. Remote Sens. 45(5), 839–852 (2007)

    Article  Google Scholar 

  11. Bunyak, F., Palaniappan, K., Nath, S.K., Baskin, T.I., Dong, G.: Quantitative cell motility for in vitro wound healing using level set-based active contour tracking. In: Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging, pp. 1040–1043

  12. Hidemasa, M., Munehiro, D., Hiroki, N., Yumi, M.: Multilevel parallelization on the Cell/B.E. for a motion JPEG 2000 encoding server. In: Proceedings of the 15th International Conference Multimedia, pp. 942–951 (2007)

  13. Liu, L., Kesavarapu, S., Connell, J., Jagmohan, A., Leem, A., Paulovicks, L., Sheinin, B. , Tang, V.L., Yeo, H.: Video analysis and compression on the STI cell broadband engine processor. In: IEEE International Conference on Multimedia and Expo (2006)

  14. Momcilovic, S., Sousa, L.: A parallel algorithm for advanced video motion estimation on multi-core architectures. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 831–836 (2008)

  15. Kumar, P., Palaniappan, K., Mittal, A., Seetharaman, G.: Parallel blob extraction using multicore cell processor. In: Advanced Concepts for Intelligent Vision Systems (ACIVS) 2009. LNCS, vol. 5807, pp. 320–332 (2009)

  16. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings CVPR, pp. 246–252 (1999)

  17. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the ICPR, vol. 2, pp. 28–31 (2004)

  18. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 255–261, Kerkyra, Corfu, Greece, 20–25 September 1999

  19. Sugano, H., Miyamoto, R.: Parallel implementation of morphological processing on CELL BE with OpenCV interface. In: Communications, Control and Signal Processing, 2008. ISCCSP 2008, pp. 578–583 (2008)

  20. Park, J.-M., Looney, C.G., Chen, H.-C.: Fast connected component labeling algorithm using a divide and conquer technique. Computer Science Dept University of Alabama and University of Nevada, Reno (2004)

  21. Chang, F., Chen, C.-J., Lu, C.-J.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Underst. 93(I2), 206–220 (2004)

    Google Scholar 

  22. Wu, K., Otoo, E., Shoshani, A.: Optimizing connected component labeling algorithms. In: Proceedings of SPIE Medical Imaging Conference 2005, San Diego, CA (2005). LBNL report LBNL-56864

  23. Fiorio, C., Gustedt, J.: Two linear time union-find strategies for image processing. Theor. Comput. Sci. 154(2), 165–181 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  24. Davis, J., Sharma, V.: Fusion-based background subtraction using contour saliency. IEEE Conf. Comput. Vis. Pattern Recognit. 3, 11–11 (2005). OTCBVS Benchmark Dataset Collection (http://www.cse.ohio-state.edu/otcbvs-bench/)

Download references

Acknowledgments

This research was supported by DRDO under Extramural Research Project Grant Number ERIP/ER/1004552/M/01/1307 in cooperation with the Government of India. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DRDO or the Government of India. The DRDO or Government of India is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The authors would like to acknowledge the help of Prof. K. Palaniappan, University of Missouri-Columbia, USA, for his valuable discussions and insights on sequential algorithms of morphology and CCL and their parallelization on CELL BE architecture for an earlier work. Also we would like to heartily thank K. Phani Sunil, B.tech student at GRIET for assisting us to carry out the experiments.

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India

    Praveen Kumar

  2. Computer Science and Engineering Department, University of Minnesota, Minneapolis, USA

    Ayush Singhal & Sanyam Mehta

  3. Department of Computer Science and Engineering, Graphics Era University, Dehradun, India

    Ankush Mittal

Authors
  1. Praveen Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayush Singhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanyam Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ankush Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Praveen Kumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, P., Singhal, A., Mehta, S. et al. Real-time moving object detection algorithm on high-resolution videos using GPUs. J Real-Time Image Proc 11, 93–109 (2016). https://doi.org/10.1007/s11554-012-0309-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-012-0309-y

Keywords

Search

Navigation