Standard
GPU accelerated likelihoods for stereo-based articulated tracking. / Friborg, Rune Møllegaard; Hauberg, Søren; Erleben, Kenny.
Trends and Topics in Computer Vision: ECCV 2010 Workshops, Heraklion, Crete, Greece, September 10-11, 2010, Revised Selected Papers, Part II. ed. / Kiriakos N. Kutulakos. Vol. Part II Springer, 2012. p. 359-371 (Lecture notes in computer science, Vol. 6554).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Friborg, RM, Hauberg, S
& Erleben, K 2012,
GPU accelerated likelihoods for stereo-based articulated tracking. in KN Kutulakos (ed.),
Trends and Topics in Computer Vision: ECCV 2010 Workshops, Heraklion, Crete, Greece, September 10-11, 2010, Revised Selected Papers, Part II. vol. Part II, Springer, Lecture notes in computer science, vol. 6554, pp. 359-371, Workshop on Computer Vision on GPUs, Heraklion, Greece,
10/09/2010.
https://doi.org/10.1007/978-3-642-35740-4_28APA
Friborg, R. M., Hauberg, S.
, & Erleben, K. (2012).
GPU accelerated likelihoods for stereo-based articulated tracking. In K. N. Kutulakos (Ed.),
Trends and Topics in Computer Vision: ECCV 2010 Workshops, Heraklion, Crete, Greece, September 10-11, 2010, Revised Selected Papers, Part II (Vol. Part II, pp. 359-371). Springer. Lecture notes in computer science Vol. 6554
https://doi.org/10.1007/978-3-642-35740-4_28Vancouver
Friborg RM, Hauberg S
, Erleben K.
GPU accelerated likelihoods for stereo-based articulated tracking. In Kutulakos KN, editor, Trends and Topics in Computer Vision: ECCV 2010 Workshops, Heraklion, Crete, Greece, September 10-11, 2010, Revised Selected Papers, Part II. Vol. Part II. Springer. 2012. p. 359-371. (Lecture notes in computer science, Vol. 6554).
https://doi.org/10.1007/978-3-642-35740-4_28Author
Friborg, Rune Møllegaard ; Hauberg, Søren ; Erleben, Kenny. / GPU accelerated likelihoods for stereo-based articulated tracking. Trends and Topics in Computer Vision: ECCV 2010 Workshops, Heraklion, Crete, Greece, September 10-11, 2010, Revised Selected Papers, Part II. editor / Kiriakos N. Kutulakos. Vol. Part II Springer, 2012. pp. 359-371 (Lecture notes in computer science, Vol. 6554).
Bibtex
@inproceedings{53a8a0d0c1d111df825b000ea68e967b,
title = "GPU accelerated likelihoods for stereo-based articulated tracking",
abstract = "For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously requiredseveral minutes, are now performed in few seconds.",
author = "Friborg, {Rune M{\o}llegaard} and S{\o}ren Hauberg and Kenny Erleben",
year = "2012",
doi = "10.1007/978-3-642-35740-4_28",
language = "English",
isbn = "978-3-642-35739-8",
volume = "Part II",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "359--371",
editor = "Kutulakos, {Kiriakos N.}",
booktitle = "Trends and Topics in Computer Vision",
address = "Switzerland",
note = "null ; Conference date: 10-09-2010 Through 11-09-2010",
}
RIS
TY - GEN
T1 - GPU accelerated likelihoods for stereo-based articulated tracking
AU - Friborg, Rune Møllegaard
AU - Hauberg, Søren
AU - Erleben, Kenny
PY - 2012
Y1 - 2012
N2 - For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously requiredseveral minutes, are now performed in few seconds.
AB - For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously requiredseveral minutes, are now performed in few seconds.
U2 - 10.1007/978-3-642-35740-4_28
DO - 10.1007/978-3-642-35740-4_28
M3 - Article in proceedings
SN - 978-3-642-35739-8
VL - Part II
T3 - Lecture notes in computer science
SP - 359
EP - 371
BT - Trends and Topics in Computer Vision
A2 - Kutulakos, Kiriakos N.
PB - Springer
Y2 - 10 September 2010 through 11 September 2010
ER -
