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CONCLUSIONS
In this paper we have presented a new 3D lidar dataset
oriented to people detection in indoor environments.
We consider that the differences between scenes cover
a wide range of situations and problems that can oc-
cur in this kind of locations. In addition, we have
tested simple algorithms to demonstrate that these dif-
ferences are reflected in the classification and tracking
accuracy of each sequence.
The current dataset is complex and challenging
enough to test different people detection and tracking
algorithms. However, we plan to extend it in the near
future with new sequences in different large indoor
places (hallways and halls in different buildings).
ACKNOWLEDGEMENTS
This work has been partially funded by FEDER funds
and the Spanish Government (MICINN) through
projects TIN2013-46638-C3-3-P, TIN2015-65686-
C5-3-R, and DPI2013-40534-R and by Consejer
´
ıa
de Educaci
´
on, Cultura y Deportes of the JCCM re-
gional government through project PPII-2014-015-P.
Cristina Romero-Gonz
´
alez is funded by the MECD
grant FPU12/04387.
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InLiDa: A 3D Lidar Dataset for People Detection and Tracking in Indoor Environments
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