Trade-off Between GPGPU based Implementations of Multi Object Tracking Particle Filter

Petr Jecmen, Frederic Lerasle, Alhayat Ali Mekonnen

Abstract

In this work, we present the design, analysis and implementation of a decentralized particle filter (DPF) for multiple object tracking (MOT) on a graphics processing unit (GPU). We investigate two variants of the implementation, their advantages and caveats in terms of scaling with larger particle numbers and performance on several datasets. First we compare the precision of our GPU implementation with standard CPU version. Next we compare performance of the GPU variants under different scenarios. The results show the GPU variant leads to a five fold speedup on average (in best cases the speedup reaches a factor of 18) over the CPU variant while keeping similar tracking accuracy and precision.

References

  1. Bernardin, K. and Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The clear mot metrics. EURASIP Journal on Image and Video Processing.
  2. Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., and Gool, L. V. (2011). Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(9):1820-1833.
  3. Chao, M. A., Chu, C. Y., Chao, C. H., and Wu, A. Y. (2010). Efficient parallelized particle filter design on cuda. In 2010 IEEE Workshop On Signal Processing Systems, pages 299-304.
  4. Chitchian, M., van Amesfoort, A. S., Simonetto, A., Keviczky, T., and Sips, H. J. (2013). Adapting particle filter algorithms to many-core architectures. In s.n., editor, Proceedings of the 2013 IEEE 27th International Parallel and Distributed Processing Symposium (IPDPS), pages 427-438. IEEE Computer Society.
  5. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), volume 1, pages 886-893 vol. 1.
  6. Dollár, P., Appel, R., Belongie, S., and Perona, P. (2014). Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8):1532-1545.
  7. Dollár, P., Wojek, C., Schiele, B., and Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 34(4):743-761.
  8. Doucet, A., Godsill, S., and Andrieu, C. (2000). On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 10(3):197-208.
  9. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell., 32(9):1627-1645.
  10. Gerónimo Gomez, D., Lerasle, F., and López Pen˜a, A. M. (2012). State-Driven Particle Filter for Multi-person Tracking, pages 467-478. Springer Berlin Heidelberg, Berlin, Heidelberg.
  11. Hendeby, G., Karlsson, R., and Gustafsson, F. (2010). Particle filtering: The need for speed. EURASIP J. Adv. Signal Process, 2010:22:1-22:9.
  12. Hirabayashi, M., Kato, S., Edahiro, M., Takeda, K., Kawano, T., and Mita, S. (2013). Gpu implementations of object detection using hog features and deformable models. In 2013 IEEE 1st International Conference on Cyber-Physical Systems, Networks, and Applications, pages 106-111. IEEE.
  13. Isard, M. and Blake, A. (1998). Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision, 29:5-28.
  14. Itu, L. M., Suciu, C., Moldoveanu, F., and Postelnicu, A. (2011). Comparison of single and double floating point precision performance for tesla architecture gpus. Bulletin of the Transilvania University of Brov Series I: Engineering Sciences, 4:70-86.
  15. Khan, Z., Balch, T., and Dellaert, F. (2005). MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11):1805-1819.
  16. Li, T., Bolic, M., and Djuric, P. M. (2015). Resampling methods for particle filtering: Classification, implementation, and strategies. IEEE Signal Processing Magazine, 32(3):70-86.
  17. Li, Y., Ai, H., Yamashita, T., Lao, S., and Kawade, M. (2008). Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(10):1728-1740.
  18. Medeiros, H., Park, J., and Kak, A. (2008). A parallel color-based particle filter for object tracking. In Computer Vision and Pattern Recognition Workshops, 2008. CVPRW 7808. IEEE Computer Society Conference on, pages 1-8.
  19. Nickolls, J., Buck, I., Garland, M., and Skadron, K. (2008). Scalable parallel programming with CUDA. Queue, 6(2):40-53.
  20. Perez, P., Vermaak, J., and Blake, A. (2004). Data fusion for visual tracking with particles. Proceedings of the IEEE, 92(3):495-513.
  21. Rost, R. J. (2006). OpenGL Shading Language. Addison Wesley Professional.
  22. Rosn, O., Medvedev, A., and Ekman, M. (2010). Speedup and tracking accuracy evaluation of parallel particle filter algorithms implemented on a multicore architecture. In 2010 IEEE International Conference on Control Applications, pages 440-445.
  23. Rymut, B. and Kwolek, B. (2010). GPU-Accelerated Object Tracking Using Particle Filtering and Appearance-Adaptive Models, pages 337-344. Springer Berlin Heidelberg, Berlin, Heidelberg.
  24. Sudowe, P. and Leibe, B. (2011). Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video. In International Conference on Computer Vision Systems (ICVS'11).
  25. Zhang, J., Presti, L. L., and Sclaroff, S. (2012). Online multi-person tracking by tracker hierarchy. In Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on, pages 379-385. IEEE.
Download


Paper Citation


in Harvard Style

Jecmen P., Lerasle F. and Ali Mekonnen A. (2017). Trade-off Between GPGPU based Implementations of Multi Object Tracking Particle Filter . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 123-131. DOI: 10.5220/0006092301230131


in Bibtex Style

@conference{visapp17,
author={Petr Jecmen and Frederic Lerasle and Alhayat Ali Mekonnen},
title={Trade-off Between GPGPU based Implementations of Multi Object Tracking Particle Filter},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={123-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006092301230131},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Trade-off Between GPGPU based Implementations of Multi Object Tracking Particle Filter
SN - 978-989-758-227-1
AU - Jecmen P.
AU - Lerasle F.
AU - Ali Mekonnen A.
PY - 2017
SP - 123
EP - 131
DO - 10.5220/0006092301230131