Spatiotemporal Models of Human Activity for Robotic Patrolling

Vintr, Tomáš; Eyisoy, Kerem; Vintrová, Vanda; Yan, Zhi; Ruichek, Yassine; Krajník, Tomáš

doi:10.1007/978-3-030-14984-0_5

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…The methods found in the literature can be divided into a few overlapping groups of approaches. Compared to the related work section, we excluded a group of methods incorporating short-term dynamics and included a time series forecasting approach that estimates the number of people over the whole map at a specific time: 1) spatial-only models ( Section 2.1 ) that do not take time into account ( Kucner et al, 2016 ; Senanayake and Ramos, 2018 ), 2) time series forecasting methods that do not take the structure of the space into account ( Vintr et al, 2018 ), 3) partially discrete and partially continuous models, ( Section 2.3.3 ), which incorporate continuous models in the cells of a predefined grid ( Krajnik et al, 2014a ; Molina et al, 2018 ). 4) methods that model spatial and temporal features separately ( Section 2.3.1 ) and understand them as independent ( Bennetts et al, 2019 ; Kubiš, 2020 ), 5) continuous spatio-temporal methods ( Section 2.4.2 ) that model the spatio-temporal phenomena together ( Vintr et al, 2019b ; Krajník et al, 2019 ), and 6) continuous spatio-temporal methods ( Section 2.4.1 ) that model the temporal evolution of the continuous spatial model ( Zhou et al, 2015 ; Zhou and Matteson, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…2) time series forecasting methods that do not take the structure of the space into account ( Vintr et al, 2018 ),…”

Section: Discussionmentioning

confidence: 99%

“…The authors then showed in experiments that the proposed method is equal to or better than FreMEn when applied to the robotics tasks. In the study by ( Vintr et al, 2018 ), Warped hypertime was applied to the task of detecting anomalies. It was proved that the model learning on binary data is faster and converges to a better quality model than in other state-of-the-art methods.…”

Section: Related Workmentioning

confidence: 99%

“…It was not computationally rational to calibrate the model using a grid of aggregations similar to the previous work ( Vintr et al, 2019a ). Instead, the authors extended the data with “negative measurements;” they added random noise labeled as “not a human.” Two models (humans and artificial nonhumans) were learned, and the “calibration” was done similarly to the approach used for binary data ( Vintr et al, 2018 ). Although the addition of artificial zeros is meaningful in some two-dimensional spatial cases ( O’Callaghan and Ramos, 2012 ), generally, it is not a convenient approach—weigh how many possibilities of “not detected velocity” one has to take into consideration.…”

Section: Related Workmentioning

confidence: 99%

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Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

et al. 2022

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Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.

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Section: Discussionmentioning

confidence: 99%

“…2) time series forecasting methods that do not take the structure of the space into account ( Vintr et al, 2018 ),…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

et al. 2022

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“…This is caused by the fact that the modeled events are sparse, and the process generating them is not stationary. To deal with the problem, we proposed in our previous works to we use a "warped-hypertime" projection of the time line into a closed subset of multidimensional vector space, where each pair of dimensions would represent one periodicity [38], [39], [40], [41], [42]. Then, we create a model characterising the probability distribution of spatiodirection-temporal events in the vector space extended by the warped hypertime.…”

Section: Methods Descriptionmentioning

confidence: 99%

Time-varying Pedestrian Flow Models for Service Robots

Vintr¹,

Molina

Senanayake

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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We present a human-centric spatio-temporal 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 by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

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Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

Krajník

Vintr

Molina

et al. 2019

IEEE Robot. Autom. Lett.

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This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudoperiodic variations caused by human activities. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The method extends the given spatial model with a set of wrapped dimensions that represent the periodicities of observed changes. By performing clustering over this extended representation, we obtain a model that allows us to predict future states of both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art.

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Spatiotemporal Models of Human Activity for Robotic Patrolling

Cited by 3 publications

References 19 publications

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

Time-varying Pedestrian Flow Models for Service Robots

Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

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