Uncovering Abnormal Behavior Patterns from Mobility Trajectories
Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior pa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/10/3520 |
| _version_ | 1797533581789102080 |
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| author | Hao Wu Xuehua Tang Zhongyuan Wang Nanxi Wang |
| author_facet | Hao Wu Xuehua Tang Zhongyuan Wang Nanxi Wang |
| author_sort | Hao Wu |
| collection | DOAJ |
| description | Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals’ activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns. |
| first_indexed | 2024-03-10T11:17:35Z |
| format | Article |
| id | doaj.art-ed03a1cdbbb64ff0b28bfda3765ff7fb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:17:35Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ed03a1cdbbb64ff0b28bfda3765ff7fb2023-11-21T20:20:35ZengMDPI AGSensors1424-82202021-05-012110352010.3390/s21103520Uncovering Abnormal Behavior Patterns from Mobility TrajectoriesHao Wu0Xuehua Tang1Zhongyuan Wang2Nanxi Wang3National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, ChinaNational Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, ChinaNational Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, ChinaUsing personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals’ activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns.https://www.mdpi.com/1424-8220/21/10/3520mobility trajectoryabnormal behavior patternLSTM-based methodspatiotemporal characteristic |
| spellingShingle | Hao Wu Xuehua Tang Zhongyuan Wang Nanxi Wang Uncovering Abnormal Behavior Patterns from Mobility Trajectories Sensors mobility trajectory abnormal behavior pattern LSTM-based method spatiotemporal characteristic |
| title | Uncovering Abnormal Behavior Patterns from Mobility Trajectories |
| title_full | Uncovering Abnormal Behavior Patterns from Mobility Trajectories |
| title_fullStr | Uncovering Abnormal Behavior Patterns from Mobility Trajectories |
| title_full_unstemmed | Uncovering Abnormal Behavior Patterns from Mobility Trajectories |
| title_short | Uncovering Abnormal Behavior Patterns from Mobility Trajectories |
| title_sort | uncovering abnormal behavior patterns from mobility trajectories |
| topic | mobility trajectory abnormal behavior pattern LSTM-based method spatiotemporal characteristic |
| url | https://www.mdpi.com/1424-8220/21/10/3520 |
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