Identifying Unusual Human Movements Using Multi-Agent and Time-Series Outlier Detection Techniques
Identifying Unusual Human Movements Using Multi-Agent and Time-Series Outlier Detection Techniques
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DOI：
10.1109/icarc57651.2023.10145617
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
P. Asanka;Chathura Rajapakshe;Masakazu Takahashi
中科院分区：
文献类型：
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作者：
P. Asanka;Chathura Rajapakshe;Masakazu Takahashi
本文介绍了知识驱动的多智能体技术，用于在人类流动性背景下进行时间序列分析的自动机器学习。本研究的主要目的是识别不寻常的人的流动性使用时间序列离群检测技术与更有效的多智能体系统。异常人体运动的检测对于许多领域都有帮助，例如安全、营销和健康。本研究使用了2019年12月至2020年11月期间日本广岛的移动的数据集。将移动的数据集转换为日本广岛多个地点的时间序列。由于时间序列的参数选取不同，采用了消息空间多智能体技术。子代理被引入用于重复删除、缺失数据替换和离群值检测。通过引入多个处理代理和一个控制代理来预测缺失值，提高了模型的效率。最后，使用季节趋势分解技术，识别出异常的运动，并将异常的人体运动与假期一起绘制出来。在所有位置检测到多个离群点，并且在所选位置检测到一百多个离群点。
This research paper has introduced knowledge-driven multi-agent technology for automated machine learning in time series analysis in the context of human mobility. The main objective of this research is to identify unusual human mobility using Time Series outlier detection techniques with a more efficient multi-agent system. Detection of unusual human movement can be helpful for many domains, such as security, marketing, and health. A mobile dataset in Hiroshima, Japan between 2019-December to 2020-November was used for this research. The mobile dataset was converted to time series for multiple locations in Hiroshima, Japan. Since many different parameters are selected for time series, the message space multi-agent technique is used. Sub agents are introduced for duplicate removal, missing data replacement, and outlier detection. Multiple processing agents and a control agent were introduced to predict the missing values to improve the efficiency of the model. Finally, using the Seasonal-Trend decomposition techniques, unusual movements are identified, and unusual human movements are plotted with the holidays. Multiple outlier points were detected for all the locations, and there were more than a hundred outlier points were detected for the selected locations.