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An overview on trajectory outlier detection

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Abstract

The task of trajectory outlier detection is to discover trajectories or their segments which differ substantially from or are inconsistent with the remaining set. In this paper, we make an overview on trajectory outlier detection algorithms from three aspects. Firstly, algorithms considering multi-attribute. In this kind of algorithms, as many key attributes as possible, such as speed, direction, position, time, are explored to represent the original trajectory and to compare with the others. Secondly, suitable distance metric. Many researches try to find or develop suitable distance metric which can measure the divergence between trajectories effectively and reliably. Thirdly, other studies attempt to improve existing algorithms to find outliers with less time and space complexity, and even more reliable. In this paper, we survey and summarize some classic trajectory outlier detection algorithms. In order to provide an overview, we analyze their features from the three dimensions above and discuss their benefits and shortcomings. It is hope that this review will serve as the steppingstone for those interested in advancing moving object outlier detection.

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Acknowledgements

We thank Dr. Daxing Li for his contribution in this paper. This work was supported by the National Natural Science Foundation of China (with grants of 71774159 and U1610124), the State’s Key Project of Research and Development Plan (with grant of 2016YFC0600908), the Fundamental Research Funds for the Central Universities, China (with grant of 2015XKMS085), and Guangxi Key Laboratory of Trusted Software (with grants of KX201613).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, 221116, Jiangsu, China

    Fanrong Meng, Guan Yuan, Shaoqian Lv, Zhixiao Wang & Shixiong Xia

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, 221116, Jiangsu, China

    Guan Yuan

  3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Guan Yuan

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  1. Fanrong Meng
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Correspondence to Guan Yuan or Zhixiao Wang.

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Fanrong Meng and Guan Yuan have contributed equally to this paper.

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Meng, F., Yuan, G., Lv, S. et al. An overview on trajectory outlier detection. Artif Intell Rev 52, 2437–2456 (2019). https://doi.org/10.1007/s10462-018-9619-1

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