원문정보
초록
영어
Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clusteringbased anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.
목차
1. Introduction
2. The Abnormal Worker Movement Detection System
2.1 Transmitting Location Information by Using the MQTT Protocol
2.2 Handling Data Stream in Apache Spark
2.3 The Anomaly Detection Algorithm
2.4 Client’s Request Handler
3. Experimental Results
3.1 Experimental Setup
3.2 Experimental Results
4. Conclusion
References