IMPROVING 3D PEDESTRIAN DETECTION FOR WEARABLE SENSOR DATA WITH 2D HUMAN POSE

Kamalasanan, V.; Feng, Y.; Sester, M.

doi:https://doi.org/10.5194/isprs-annals-V-4-2022-219-2022

Articles | Volume V-4-2022

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Articles | Volume V-4-2022

https://doi.org/10.5194/isprs-annals-V-4-2022-219-2022

Articles | Volume V-4-2022

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18 May 2022

| 18 May 2022

IMPROVING 3D PEDESTRIAN DETECTION FOR WEARABLE SENSOR DATA WITH 2D HUMAN POSE

V. Kamalasanan, Y. Feng, and M. Sester

V. Kamalasanan

Institute for Cartography and Geoinformatics, Leibniz Universität Hannover, 30167 Hannover, Germany

Y. Feng

Institute for Cartography and Geoinformatics, Leibniz Universität Hannover, 30167 Hannover, Germany

M. Sester

Institute for Cartography and Geoinformatics, Leibniz Universität Hannover, 30167 Hannover, Germany

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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 185–192, https://doi.org/10.5194/isprs-archives-XLII-4-185-2018,https://doi.org/10.5194/isprs-archives-XLII-4-185-2018, 2018

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A. Leichter, D. Wittich, F. Rottensteiner, M. Werner, and M. Sester

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 335–342, https://doi.org/10.5194/isprs-archives-XLII-4-335-2018,https://doi.org/10.5194/isprs-archives-XLII-4-335-2018, 2018

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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 565–572, https://doi.org/10.5194/isprs-archives-XLII-4-565-2018,https://doi.org/10.5194/isprs-archives-XLII-4-565-2018, 2018

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Y. Feng, C. Brenner, and M. Sester

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Fabian Bock, Karen Xia, and Monika Sester

Proc. Int. Cartogr. Assoc., 1, 12, https://doi.org/10.5194/ica-proc-1-12-2018,https://doi.org/10.5194/ica-proc-1-12-2018, 2018

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Dirk Burghardt, Wolfgang Nejdl, Jochen Schiewe, and Monika Sester

Proc. Int. Cartogr. Assoc., 1, 15, https://doi.org/10.5194/ica-proc-1-15-2018,https://doi.org/10.5194/ica-proc-1-15-2018, 2018

Areal rainfall estimation using moving cars – computer experiments including hydrological modeling

Ehsan Rabiei, Uwe Haberlandt, Monika Sester, Daniel Fitzner, and Markus Wallner

Hydrol. Earth Syst. Sci., 20, 3907–3922, https://doi.org/10.5194/hess-20-3907-2016,https://doi.org/10.5194/hess-20-3907-2016, 2016

Short summary

The value of using moving cars for rainfall measurement purposes (RCs) was investigated with laboratory experiments by Rabiei et al. (2013). They analyzed the Hydreon and Xanonex optical sensors against different rainfall intensities. A continuous investigation of using RCs with the derived uncertainties from laboratory experiments for areal rainfall estimation as well as implementing the data in a hydrological model are addressed in this study.

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INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

S. Zourlidou and M. Sester

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 269–276, https://doi.org/10.5194/isprs-archives-XLI-B2-269-2016,https://doi.org/10.5194/isprs-archives-XLI-B2-269-2016, 2016

3D FEATURE POINT EXTRACTION FROM LIDAR DATA USING A NEURAL NETWORK

Y. Feng, A. Schlichting, and C. Brenner

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 563–569, https://doi.org/10.5194/isprs-archives-XLI-B1-563-2016,https://doi.org/10.5194/isprs-archives-XLI-B1-563-2016, 2016

Rainfall estimation using moving cars as rain gauges – laboratory experiments

E. Rabiei, U. Haberlandt, M. Sester, and D. Fitzner

Hydrol. Earth Syst. Sci., 17, 4701–4712, https://doi.org/10.5194/hess-17-4701-2013,https://doi.org/10.5194/hess-17-4701-2013, 2013

IMPROVING 3D PEDESTRIAN DETECTION FOR WEARABLE SENSOR DATA WITH 2D HUMAN POSE

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