TY - GEN
T1 - Time-varying pedestrian flow models for service robots
AU - Vintr, Tomas
AU - Molina, Sergi
AU - Senanayake, Ransalu
AU - Broughton, George
AU - Yan, Zhi
AU - Ulrich, Jiri
AU - Kucner, Tomasz Piotr
AU - Swaminathan, Chittaranjan Srinivas
AU - Majer, Filip
AU - Stachova, Maria
AU - Lilienthal, Achim J.
AU - Krajnik, Tomas
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.
AB - We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.
UR - http://www.scopus.com/inward/record.url?scp=85074395312&partnerID=8YFLogxK
U2 - 10.1109/ECMR.2019.8870909
DO - 10.1109/ECMR.2019.8870909
M3 - Conference contribution
AN - SCOPUS:85074395312
T3 - 2019 European Conference on Mobile Robots, ECMR 2019 - Proceedings
BT - 2019 European Conference on Mobile Robots, ECMR 2019 - Proceedings
A2 - Preucil, Libor
A2 - Behnke, Sven
A2 - Kulich, Miroslav
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 European Conference on Mobile Robots, ECMR 2019
Y2 - 4 September 2019 through 6 September 2019
ER -