Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

Author:

Xie Bin1,Guo Hui2,Zheng Guo3

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

2. Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543002, China

3. Project Management Department, East China Institute of Computing Technology, Shanghai 201808, China

Abstract

As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

Funder

National Natural Science Foundation of China

Guangxi Key Laboratory of Machine Vision and Intelligent Control

Publisher

MDPI AG

Reference31 articles.

1. Research Progress on Spatiotemporal Trajectory Big Data Pattern Mining;Genlin;Data Acquis. Process.,2015

2. Lee, J.G., Han, J., and Li, X. (2008, January 7–12). Trajectory outlier detection: A partition-and-detect framework. Proceedings of the IEEE International Conference on Data Engineering, Cancun, Mexico.

3. An Efficient Anomaly Trajectory Detection Algorithm Based on R-Tree;Liu;J. Softw.,2009

4. Ge, Y., Xiong, H., Zhou, Z.H., Ozdemir, H., Yu, J., and Lee, K.C. (2010, January 3–7). TOP-EYE: Top-k evolving trajectory outlier detection. Proceedings of the ACM International Conference on Information & Knowledge Management, Shanghai, China.

5. Bu, Y., Chen, L., Fu, A.W.C., and Liu, D. (2019, January 4–8). Efficient anomaly monitoring over moving object trajectory streams. Proceedings of the ACM Sigkdd International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.

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