Mining regular behaviors based on multidimensional trajectories

Pan, Xinlong; He, Yong; Wang, Haipeng; Xiong, Wei; Peng, Xuan

doi:10.1016/j.eswa.2016.09.015

Cited by 27 publications

(11 citation statements)

References 21 publications

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“…Geometric measures are the most commonly used across all application domains. Several surveys have considered the topic of trajectory analysis and comparison (Morris and Trivedi, 2008; Pan et al, 2016; Quehl et al, 2017; Zhang et al, 2006; Zheng, 2015) where, based on the previous ones, only the recent survey by Quehl et al (2017) specifically considers geometric similarity measures for trajectory prediction evaluation. In addition to that, we review the probabilistic metrics and the assessment of distributions with geometric methods in Section 7.1.2, and the experiments to evaluate robustness in Section 7.1.3.…”

Section: Motion Prediction Evaluationmentioning

confidence: 99%

Human motion trajectory prediction: a survey

Rudenko

Palmieri

Herman³

et al. 2020

The International Journal of Robotics Research

604

306

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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: Motion Prediction Evaluationmentioning

confidence: 99%

Human motion trajectory prediction: a survey

Rudenko

Palmieri

Herman³

et al. 2020

The International Journal of Robotics Research

604

306

View full text Add to dashboard Cite

show abstract

“…For instance, Gao et al [ 2 ] proposed a pattern recognition method for the analysis and clustering of ship trajectories and behaviors based on AIS trajectories. Pan et al [ 3 ] introduced a multidimensional trajectory mining and clustering algorithm based on the ship’s type, position, speed, and heading. Zhao et al [ 4 ] applied a DBSCAN algorithm to large AIS trajectory data for characterizing and clustering ship trajectories, which has proven to be successful in determining maritime traffic patterns.…”

Section: Related Workmentioning

confidence: 99%

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

Suo

Zhang

et al. 2022

Sensors

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The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels’ navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships’ behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents.

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“…7.1.1 Geometric Accuracy Metrics Geometric measures are the most commonly used across all application domains. Several surveys have considered the topic of trajectory analysis and comparison (Zhang et al 2006;Morris and Trivedi 2008;Zheng 2015;Quehl et al 2017;Pan et al 2016) where, based on the previous ones, only the recent survey by Quehl et al (2017) specifically considers similarity measures for trajectory prediction evaluation. Summarizing (Morris and Trivedi 2008;Quehl et al 2017), we consider eight metrics:…”

Section: Performance Metricsmentioning

confidence: 99%

Human Motion Trajectory Prediction: A Survey

Rudenko,

Palmieri,

Herman

et al. 2019

Preprint

View full text Add to dashboard Cite

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 paper 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.

show abstract

Mining regular behaviors based on multidimensional trajectories

Cited by 27 publications

References 21 publications

Human motion trajectory prediction: a survey

Human motion trajectory prediction: a survey

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

Human Motion Trajectory Prediction: A Survey

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