Modelling and Predicting Rhythmic Flow Patterns in Dynamic Environments

Mellado, Sergi Molina; Cielniak, Grzegorz; Krajník, Tomáš; Duckett, Tom

doi:10.1007/978-3-319-96728-8_12

Cited by 23 publications

(34 citation statements)

References 17 publications

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“…Learning local motion patterns, such as probabilities of transitions between cells on a grid map (Figure 9(a)), is a simple, commonly used technique for making sequential predictions (Ballan et al, 2016; Kruse and Wahl, 1998; Kucner et al, 2013; Molina et al, 2018; Tadokoro et al, 1993; Thompson et al, 2009; Wang et al, 2015, 2016).…”

Section: Pattern-based Approachesmentioning

confidence: 99%

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Human motion trajectory prediction: a survey

Rudenko

Palmieri

Herman³

et al. 2020

The International Journal of Robotics Research

517

282

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With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

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Section: Pattern-based Approachesmentioning

confidence: 99%

“…It encodes functional properties of the environment (i.e., direction and speed of the targets, crossing frequency for each patch, identification of routing points). Molina et al (2018) addressed periodic temporal variations in the learned transition patterns, e.g., based on the time of the day.…”

Section: Pattern-based Approachesmentioning

confidence: 99%

Human motion trajectory prediction: a survey

Rudenko

Palmieri

Herman³

et al. 2020

The International Journal of Robotics Research

517

282

View full text Add to dashboard Cite

show abstract

“…Another discrete, grid-based model can be found in [12], where authors predict the paths of people based on an input-output Markov model associated with each cell. The authors of [29] assume the pedestrian flows change over time in a periodic fashion, and associate each cell of their grid with directional information enhanced by FreMEn [7].…”

Section: Related Workmentioning

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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“…Our exploration method is built on the concept presented in [16], which creates a time-dependent probabilistic map that is able to model and predict patterns of people in indoor environments. However, in [16], all the models were built assuming that the environment is fully observable both in time and space, which is unlikely in a real-world scenario.…”

Section: Related Workmentioning

confidence: 99%

“…To build the Poisson processes in each cell we impose a global period of 1 day. This value comes from our previous work [16], which considered the same two datasets, where it was found that a daily rhythmic pattern gave the best fit. These periodicities were provided by the FreMEn tool, which analyses the temporal patterns of the data provided to it.…”

Section: B Model Parametersmentioning

confidence: 99%

Go with the Flow: Exploration and Mapping of Pedestrian Flow Patterns from Partial Observations

Molina

Cielniak

Duckett

2019

2019 International Conference on Robotics and Automation (ICRA)

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Understanding how people are likely to behave in an environment is a key requirement for efficient and safe robot navigation. However, mobile platforms are subject to spatial and temporal constraints, meaning that only partial observations of human activities are typically available to a robot, while the activity patterns of people in a given environment may also change at different times. To address these issues we present as the main contribution an exploration strategy for acquiring models of pedestrian flows, which decides not only the locations to explore but also the times when to explore them. The approach is driven by the uncertainty from multiple Poisson processes built from past observations. The approach is evaluated using two long-term pedestrian datasets, comparing its performance against uninformed exploration strategies. The results show that when using the uncertainty in the exploration policy, model accuracy increases, enabling faster learning of human motion patterns.

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Modelling and Predicting Rhythmic Flow Patterns in Dynamic Environments

Cited by 23 publications

References 17 publications

Human motion trajectory prediction: a survey

Human motion trajectory prediction: a survey

Time-varying Pedestrian Flow Models for Service Robots

Go with the Flow: Exploration and Mapping of Pedestrian Flow Patterns from Partial Observations

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