Skip to main content
SpringerLink
Log in

Performance Evaluation of Selected Parallel Object Detection and Tracking Algorithms on an Embedded GPU Platform

  • Conference paper
  • First Online:
Multimedia Communications, Services and Security (MCSS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 785))

Abstract

Performance evaluation of selected complex video processing algorithms, implemented on a parallel, embedded GPU platform Tegra X1, is presented. Three algorithms were chosen for evaluation: a GMM-based object detection algorithm, a particle filter tracking algorithm and an optical flow based algorithm devoted to people counting in a crowd flow. The choice of these algorithms was based on their computational complexity and parallel structure. The aim of the experiments was to assess whether the current generation of low-power, mobile GPUs has sufficient power for running live analysis of video surveillance streams, e.g. in smart cameras, while maintaining energy consumption at a reasonable level. Tests were performed with both a synthetic benchmark and a real video surveillance recording. It was found that the computational power of the tested platform is sufficient for running operations such as background subtraction, but in case of more complex algorithms, such as tracking with particle filters, performance is not satisfactory because of inefficient memory architecture which stalls the processing.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. CUDA C best practices guide. http://docs.nvidia.com/cuda/cuda-c-best-practices-guide. Accessed 30 Dec 2016

  2. NVIDIA Jetson TX1 module. http://www.nvidia.com/object/jetson-tx1-module.html. Accessed 30 Dec 2016

  3. OpenCV CUDA accelerated computer vision. http://docs.opencv.org/3.0-last-rst/modules/cuda/doc/introduction.html. Accessed 30 Dec 2016

  4. Adarve, J.D., Mahony, R.: A filter formulation for computing real time optical flow. IEEE Robot. Autom. Lett. 1(2), 1192–1199 (2016)

    Article  Google Scholar 

  5. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Sig. Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

  6. Belbachir, A.N.: Smart Cameras. Springer, Boston (2010). doi:10.1007/978-1-4419-0953-4

    Book  Google Scholar 

  7. Bouguet, J.Y.: Pyramidal implementation of the Lucas Kanade feature tracker. Intel Corporation, Microprocessor Research Labs (2000)

    Google Scholar 

  8. Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)

    Article  Google Scholar 

  9. Czyżewski, A., Bratoszewski, P., Ciarkowski, A., Cichowski, J., Lisowski, K., Szczodrak, M., Szwoch, G., Krawczyk, H.: Massive surveillance data processing with supercomputing cluster. Inf. Sci. 296, 322–344 (2015)

    Article  Google Scholar 

  10. Czyzewski, A., Szwoch, G., Dalka, P., Szczuko, P., Ciarkowski, A., Ellwart, D., Merta, T., Lopatka, K., Kulasek, L., Wolski, J.: Multi-stage video analysis framework. In: Video Surveillance, pp. 147–172 (2011)

    Google Scholar 

  11. Gordon, N., Ristic, B., Arulampalam, S.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, London (2004)

    MATH  Google Scholar 

  12. Gwosdek, P., Zimmer, H., Grewenig, S., Bruhn, A., Weickert, J.: A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6554, pp. 372–383. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35740-4_29

    Chapter  Google Scholar 

  13. Hanbury, A.: A 3D-polar coordinate colour representation well adapted to image analysis. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 804–811. Springer, Heidelberg (2003). doi:10.1007/3-540-45103-X_107

    Chapter  Google Scholar 

  14. Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  15. Kim, J.S., Hwangbo, M., Kanade, T.: Parallel algorithms to a parallel hardware: designing vision algorithms for a GPU. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 862–869. IEEE (2009)

    Google Scholar 

  16. Melpignano, D., Benini, L., Flamand, E., Jego, B., Lepley, T., Haugou, G., Clermidy, F., Dutoit, D.: Platform 2012, a many-core computing accelerator for embedded SoCs: performance evaluation of visual analytics applications. In: Proceedings of the 49th Annual Design Automation Conference, DAC 2012, pp. 1137–1142. ACM, New York (2012)

    Google Scholar 

  17. Monson, J., Wirthlin, M., Hutchings, B.L.: Implementing high-performance, low-power FPGA-based optical flow accelerators in C. In: 2013 IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors, pp. 363–369. IEEE (2013)

    Google Scholar 

  18. Nummiaro, K., Koller-Meier, E., Van Gool, L.: An adaptive color-based particle filter. Image Vis. Comput. 21(1), 99–110 (2003)

    Article  MATH  Google Scholar 

  19. Pauwels, K., Van Hulle, M.M.: Realtime phase-based optical flow on the GPU. In: Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–8. IEEE (2008)

    Google Scholar 

  20. Pham, V., Vo, P., Hung, V.T., et al.: GPU implementation of extended gaussian mixture model for background subtraction. In: 2010 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp. 1–4. IEEE (2010)

    Google Scholar 

  21. Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: edge-preserving interpolation of correspondences for optical flow. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1164–1172. IEEE (2015)

    Google Scholar 

  22. Schwiegelshohn, F., Ossovski, E., Hübner, M.: A fully parallel particle filter architecture for FPGAs. In: Sano, K., Soudris, D., Hübner, M., Diniz, P.C. (eds.) ARC 2015. LNCS, vol. 9040, pp. 91–102. Springer, Cham (2015). doi:10.1007/978-3-319-16214-0_8

    Google Scholar 

  23. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252. IEEE (1999)

    Google Scholar 

  24. Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115–137 (2014)

    Article  Google Scholar 

  25. Szwoch, G.: Performance evaluation of parallel background subtraction on GPU platforms. Elektronika: konstrukcje, technologie, zastosowania 56(4), 23–27 (2015)

    Google Scholar 

  26. Szwoch, G.: Performance evaluation of the parallel object tracking algorithm employing the particle filter. In: 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 119–124. SPA, September 2016

    Google Scholar 

  27. Szwoch, G., Ellwart, D., Czyzewski, A.: Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform. J. Real Time Image Process. 11(1), 111–125 (2016)

    Article  Google Scholar 

  28. Welch, G., Bishop, G.: An introduction to the Kalman filter. Technical report TR-95041 (2004). https://www.cs.unc.edu/welch/kalman/kalmanIntro.html

  29. Wulff, J., Black, M.J.: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 120–130. IEEE (2015)

    Google Scholar 

  30. Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L 1 optical flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74936-3_22

    Chapter  Google Scholar 

  31. Zhang, F., Gao, Y., Bakos, J.D.: Lucas-Kanade optical flow estimation on the TI C66x digital signal processor. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6. IEEE, September 2014

    Google Scholar 

  32. Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)

    Article  Google Scholar 

Download references

Acknowledgment

Project financed partially by the by the Polish National Centre for Research and Development (NCBR) from the European Regional Development Fund under the Operational Programme Innovative Economy No. POIR.04.01.04-00-0089/16 entitled: INZNAK - “Intelligent road signs”.

Author information

Authors and Affiliations

  1. Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, ul. Narutowicza 11/12, 80-233, Gdansk, Poland

    Grzegorz Szwoch & Maciej Szczodrak

Authors
  1. Grzegorz Szwoch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maciej Szczodrak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grzegorz Szwoch .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Andrzej Dziech

  2. Gdańsk University of Technology, Gdansk, Poland

    Andrzej Czyżewski

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Szwoch, G., Szczodrak, M. (2017). Performance Evaluation of Selected Parallel Object Detection and Tracking Algorithms on an Embedded GPU Platform. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2017. Communications in Computer and Information Science, vol 785. Springer, Cham. https://doi.org/10.1007/978-3-319-69911-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69911-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69910-3

  • Online ISBN: 978-3-319-69911-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation