![](bg9.png)
ACKNOWLEDGMENTS
The work of P. Jecmen was supported by the Ministry
of Education of the Czech Republic within the SGS
project no. 21176/115 of the Technical University of
Liberec.
The work of F. Lerasle and A. A. Mekonnen was sup-
ported by grants from the French DGA under grant
reference SERVAT RAPID-142906073.
REFERENCES
Bernardin, K. and Stiefelhagen, R. (2008). Evaluating mul-
tiple object tracking performance: The clear mot met-
rics. EURASIP Journal on Image and Video Process-
ing.
Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier,
E., and Gool, L. V. (2011). Online multiper-
son tracking-by-detection from a single, uncalibrated
camera. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 33(9):1820–1833.
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.
Chitchian, M., van Amesfoort, A. S., Simonetto, A., Ke-
viczky, T., and Sips, H. J. (2013). Adapting particle
filter algorithms to many-core architectures. In s.n.,
editor, Proceedings of the 2013 IEEE 27th Interna-
tional Parallel and Distributed Processing Symposium
(IPDPS), pages 427–438. IEEE Computer Society.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gra-
dients for human detection. In 2005 IEEE Computer
Society Conference on Computer Vision and Pattern
Recognition (CVPR’05), volume 1, pages 886–893
vol. 1.
Doll
´
ar, P., Appel, R., Belongie, S., and Perona, P. (2014).
Fast feature pyramids for object detection. IEEE
Transactions on Pattern Analysis and Machine Intel-
ligence, 36(8):1532–1545.
Doll
´
ar, 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 Ma-
chine Intelligence (PAMI), 34(4):743–761.
Doucet, A., Godsill, S., and Andrieu, C. (2000). On se-
quential monte carlo sampling methods for bayesian
filtering. Statistics and Computing, 10(3):197–208.
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and
Ramanan, D. (2010). Object detection with discrimi-
natively trained part-based models. IEEE Trans. Pat-
tern Anal. Mach. Intell., 32(9):1627–1645.
Ger
´
onimo Gomez, D., Lerasle, F., and L
´
opez Pe
˜
na, A. M.
(2012). State-Driven Particle Filter for Multi-person
Tracking, pages 467–478. Springer Berlin Heidelberg,
Berlin, Heidelberg.
Hendeby, G., Karlsson, R., and Gustafsson, F. (2010). Par-
ticle filtering: The need for speed. EURASIP J. Adv.
Signal Process, 2010:22:1–22:9.
Hirabayashi, M., Kato, S., Edahiro, M., Takeda, K.,
Kawano, T., and Mita, S. (2013). Gpu implementa-
tions of object detection using hog features and de-
formable models. In 2013 IEEE 1st International
Conference on Cyber-Physical Systems, Networks,
and Applications, pages 106–111. IEEE.
Isard, M. and Blake, A. (1998). Condensation - conditional
density propagation for visual tracking. International
Journal of Computer Vision, 29:5–28.
Itu, L. M., Suciu, C., Moldoveanu, F., and Postelnicu,
A. (2011). Comparison of single and double float-
ing point precision performance for tesla architecture
gpus. Bulletin of the Transilvania University of Brov
Series I: Engineering Sciences, 4:70–86.
Khan, Z., Balch, T., and Dellaert, F. (2005). Mcmc-based
particle filtering for tracking a variable number of in-
teracting targets. IEEE Transactions on Pattern Anal-
ysis and Machine Intelligence, 27(11):1805–1819.
Li, T., Bolic, M., and Djuric, P. M. (2015). Resampling
methods for particle filtering: Classification, imple-
mentation, and strategies. IEEE Signal Processing
Magazine, 32(3):70–86.
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 differ-
ent life spans. Pattern Analysis and Machine Intelli-
gence, IEEE Transactions on, 30(10):1728–1740.
Medeiros, H., Park, J., and Kak, A. (2008). A paral-
lel color-based particle filter for object tracking. In
Computer Vision and Pattern Recognition Workshops,
2008. CVPRW ’08. IEEE Computer Society Confer-
ence on, pages 1–8.
Nickolls, J., Buck, I., Garland, M., and Skadron, K. (2008).
Scalable parallel programming with cuda. Queue,
6(2):40–53.
Perez, P., Vermaak, J., and Blake, A. (2004). Data fusion
for visual tracking with particles. Proceedings of the
IEEE, 92(3):495–513.
Rost, R. J. (2006). OpenGL Shading Language. Addison
Wesley Professional.
Rosn, O., Medvedev, A., and Ekman, M. (2010). Speedup
and tracking accuracy evaluation of parallel particle
filter algorithms implemented on a multicore architec-
ture. In 2010 IEEE International Conference on Con-
trol Applications, pages 440–445.
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.
Sudowe, P. and Leibe, B. (2011). Efficient Use of Geomet-
ric Constraints for Sliding-Window Object Detection
in Video. In International Conference on Computer
Vision Systems (ICVS’11).
Zhang, J., Presti, L. L., and Sclaroff, S. (2012). Online
multi-person tracking by tracker hierarchy. In Ad-
vanced Video and Signal-Based Surveillance (AVSS),
2012 IEEE Ninth International Conference on, pages
379–385. IEEE.
Trade-off Between GPGPU based Implementations of Multi Object Tracking Particle Filter
131