Figure 4: Trajectories mapped onto the rectified ground plane of
the entrance hall of a university.
analysis with respect to applications as mentioned in the intro-
duction. In future work we plan to perform more comprehensive
tests in complex scenarios.
ACKNOWLEDGEMENTS
We gratefully acknowledge the financial support by the German
Research Foundation (DFG) for the project QTrajectories under
grant HE 1822/24-1.
References
Amit, Y. and Geman, D., 1997. Shape quantization and recog-
nition with randomized trees. Neural computation 9(7),
pp. 1545-1588.
Avidan, S., 2005. Ensemble tracking. In: Proceedings IEEE Con-
ference on Computer Vision and Pattern Recognition., Vol. 2,
pp. 494—501.
Breiman, L., 2001. Random forests. Machine learning 45(1),
pp. 5-32.
Breitenstein, M., Reichlin, E, Leibe, B., Koller-Meier, E. and
Van Gool, L., 2011. Online multi-person tracking-by-detection
from a single, uncalibrated camera. IEEE Transactions on Pat-
tern Analysis and Machine Intelligence 33(9), pp. 1820-1833.
Comaniciu, D., Ramesh, V. and Meer, P., 2003. Kernel-based
object tracking. IEEE Transactions on Pattern Analysis and
Machine Intelligence 25(5), pp. 564 — 577.
Dalal, N. and Triggs, B., 2005. Histograms of oriented gradients
for human detection. In: Proceedings IEEE Conference on
Computer Vision and Pattern Recognition., Vol. 1, pp. 886—
893.
396
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Dollar, P., Wojek, C., Schiele, B. and Perona, P., 2011. Pedestrian
detection: An evaluation of the state of the art. IEEE Trans.
actions on Pattern Analysis and Machine Intelligence, 34 (4).
pp. 743-761.
Geurts, P., Ernst, D. and Wehenkel, L., 2006. Extremely random-
ized trees. Machine learning 63(1), pp. 3-42.
Grabner, H. and Bischof, H., 2006. On-line boosting and vi-
sion. In: Proceedings IEEE Conference on Computer Vision
and Pattern Recognition, Vol. 1, pp. 260—267.
Jaenen, U., Feuerhake, U., Klinger, T., Muhle, D., Sester, M,,
Haehner, J. and Heipke, C., 2012. Qtrajectories: Improving
the quality of object tracking using self-organizing camera net-
works. In: International Annals of Photogrammetry and Re-
mote Sensing (Accepted for Publication).
Lepetit, V. and Fua, P., 2006. Keypoint recognition using random-
ized trees. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 28(9), pp. 1465 —1479.
McKenna, S., Raja, Y. and Gong, S., 1999. Tracking colour ob-
jects using adaptive mixture models. Image and vision com-
puting 17(3-4), pp. 225-231.
Oza, N. and Russell, S., 2001. Online bagging and boosting. In:
In Artificial Intelligence and Statistics 2001.
Ozuysal, M., Calonder, M., Lepetit, V. and Fua, P., 2010. Fast
keypoint recognition using random ferns. IEEE Transactions
on Pattern Analysis and Machine Intelligence. 32(3), pp. 448-
461.
Saffari, A., Leistner, C., Santner, J., Godec, M. and Bischof, H.,
2009. On-line random forests. In: 3rd IEEE ICCV Workshop
on On-line Computer Vision.
Schindler, K., Ess, A., Leibe, B. and Van Gool, L., 2010. Au-
tomatic detection and tracking of pedestrians from a moving
stereo rig. ISPRS Journal of Photogrammetry and Remote
Sensing 65(6), pp. 523-537.
Sharp, T., 2008. Implementing decision trees and forests on a
gpu. ECCV pp. 595-608.
Stauffer, C. and Grimson, W., 1999. Adaptive background mix-
ture models for real-time tracking. In: Proceedings IEEE Con-
ference on Computer Vision and Pattern Recognition, pp. 246-
252.
KEY
ABST
A nev
proce:
space
other
simul
the m
railwe
advar
inforr
Recei
publi
more
in sp
consi
chang
choic
flow
planr
Cross
main
comp
Obse
unde
to tl
incre
autor
chall
other
by c
get
infor
obtai
for h
Meas
made
mod
each
inter
pede
pede
and |
impr
then