Tensor Methods for Group Pattern Discovery of Pedestrian Trajectories

Sawas, Abdullah; Abuolaim, Abdullah; Afifi, Mahmoud; Papagelis, Manos

doi:10.1109/mdm.2018.00024

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…Two comprehensive surveys are provided by Zheng [18] and Atluri et al [53]. Closely related to the problem of interest in this paper are problems that focus on mining the interactions among moving objects, over time, such as detecting pedestrian groups in trajectories [54][55][56] or determining the node centrality of moving objects in trajectory networks [20]. More recently, deep learning approaches for learning from spatiotemporal data and spatiotemporal networks have gained increasing attention [57,58].…”

Section: Trajectory Data Miningmentioning

confidence: 99%

Epidemic Spreading in Trajectory Networks

Pechlivanoglou

Sun

et al. 2022

Big Data Research

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Section: Trajectory Data Miningmentioning

confidence: 99%

Epidemic Spreading in Trajectory Networks

Pechlivanoglou

Sun

et al. 2022

Big Data Research

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“…In this paper, we provide an extensive experimental study to find small groups in pedestrian data. This is an important case in analyzing the throughput in public spaces like shopping malls [38], parks [23,50] and train stations [36], and in detecting suspect behavior in such spaces [10,20]. With the Covid-19 pandemic, the application to identifying possible transmission of a disease has become highly relevant as well.…”

Section: Our Contributionmentioning

confidence: 99%

An experimental evaluation of grouping definitions for moving entities

Wiratma

Kreveld

Löffler

et al. 2022

JOSIS

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One important pattern analysis task for trajectory data is to find a group: a set of entities that travel together over a period of time. In this paper, we compare four definitions of groups by conducting extensive experiments using various data sets. The grouping definitions are different by one or more of three different characteristics: whether they use the measured sample points or continuous movement, how distance is used to decide if entities are in the same group, and whether the duration of the group is measured cumulatively or as one contiguous time interval. We are interested in the differences between the definitions and comparisons to human-annotated data, if available. We concentrate on pedestrian data and on different crowd densities. Furthermore, we analyze the robustness of the definitions with respect to their dependence on different sampling rates. We use two types of trajectory data sets: synthetic trajectories and real-life trajectories extracted from video surveillance. We present the results of the quantitative evaluations. For experiments with real-life trajectories, we augment them with a qualitative evaluation using videos that show groups in the trajectories with a color coding.

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“…I bring some examples of them here. Sawas et al [39] efficiently discover trajectories of objects that are found in close proximity to each other for a period of time by mining group patterns of moving objects. Pechlivanoglou et al [36] devise a method that is able to simultaneously evaluate node importance metrics for all moving objects in a trajectory network.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating and forecasting the operational performance of road intersections

Nematichari

Pechlivanoglou

Papagelis

2022

Proceedings of the 30th International Conference on Advances in Geographic Information Systems

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Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of the whole transport network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data or expert opinions. However, due to the advancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies, it is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic conditions and enabling situational awareness of vehicle drivers. Towards this end, we design, develop and evaluate a system for evaluating and forecasting the operational performance of road intersections by mining streams of V2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network abstraction. In addition, the system enables interactive analysis and exploration of the various MOEs. The system architecture and methods are general and can be used in various settings requiring continuous monitoring and/or forecasting of the road network state. List of Figures1.1 Illustration of edges, junctions and lanes forming a road intersection. Our system evaluates and forecasts the operational performance of road intersections by mining streams of trajectory data. Metrics are based on well-defined MOEs.4.1 Illustration of the high-level evaluation and prediction system architecture. .

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Tensor Methods for Group Pattern Discovery of Pedestrian Trajectories

Cited by 15 publications

References 28 publications

Epidemic Spreading in Trajectory Networks

Epidemic Spreading in Trajectory Networks

An experimental evaluation of grouping definitions for moving entities

Evaluating and forecasting the operational performance of road intersections

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