Unsupervised learning trajectory anomaly detection algorithm based on deep representation
Without ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720971504 |
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| _version_ | 1832556797453402112 |
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| author | Zhongqiu Wang Guan Yuan Haoran Pei Yanmei Zhang Xiao Liu |
| author_facet | Zhongqiu Wang Guan Yuan Haoran Pei Yanmei Zhang Xiao Liu |
| author_sort | Zhongqiu Wang |
| collection | DOAJ |
| description | Without ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined features cannot be found efficiently. Meanwhile, traditional methods still cannot get rid of the limitation of space attributes. Therefore, a novel trajectory anomaly detection algorithm is present in this article. Unsupervised learning mechanism is used to overcome nonground-truth problem and deep representation method is used to represent trajectories in a comprehensive way. First, each trajectory is partitioned into segments according to its open angles, then the shallow features at each point of a segment are extracted and. In this way, each segment is represented as a feature sequence. Second, shallow features are integrated into auto-encoder-based deep feature fusion model, and the fusion feature sequences can be extracted. Third, these fused feature sequences are grouped into different clusters using a unsupervised clustering algorithm, and then segments which quite differ from others are detected as anomalies. Finally, comprehensive experiments are conducted on both synthetic and real data sets, which demonstrate the efficiency of our work. |
| format | Article |
| id | doaj-art-1942b400fae3497194d96bd225bfe012 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-1942b400fae3497194d96bd225bfe0122025-02-03T05:44:18ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-12-011610.1177/1550147720971504Unsupervised learning trajectory anomaly detection algorithm based on deep representationZhongqiu Wang0Guan Yuan1Haoran Pei2Yanmei Zhang3Xiao Liu4School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaJiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, ChinaWithout ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined features cannot be found efficiently. Meanwhile, traditional methods still cannot get rid of the limitation of space attributes. Therefore, a novel trajectory anomaly detection algorithm is present in this article. Unsupervised learning mechanism is used to overcome nonground-truth problem and deep representation method is used to represent trajectories in a comprehensive way. First, each trajectory is partitioned into segments according to its open angles, then the shallow features at each point of a segment are extracted and. In this way, each segment is represented as a feature sequence. Second, shallow features are integrated into auto-encoder-based deep feature fusion model, and the fusion feature sequences can be extracted. Third, these fused feature sequences are grouped into different clusters using a unsupervised clustering algorithm, and then segments which quite differ from others are detected as anomalies. Finally, comprehensive experiments are conducted on both synthetic and real data sets, which demonstrate the efficiency of our work.https://doi.org/10.1177/1550147720971504 |
| spellingShingle | Zhongqiu Wang Guan Yuan Haoran Pei Yanmei Zhang Xiao Liu Unsupervised learning trajectory anomaly detection algorithm based on deep representation International Journal of Distributed Sensor Networks |
| title | Unsupervised learning trajectory anomaly detection algorithm based on deep representation |
| title_full | Unsupervised learning trajectory anomaly detection algorithm based on deep representation |
| title_fullStr | Unsupervised learning trajectory anomaly detection algorithm based on deep representation |
| title_full_unstemmed | Unsupervised learning trajectory anomaly detection algorithm based on deep representation |
| title_short | Unsupervised learning trajectory anomaly detection algorithm based on deep representation |
| title_sort | unsupervised learning trajectory anomaly detection algorithm based on deep representation |
| url | https://doi.org/10.1177/1550147720971504 |
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