International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Detecting Anomalous Trajectories of Workers using Density Method

  • Lan, Doi Thi (Department of Electrical, Electronic and Computer Engineering, University of Ulsan) ;
  • Yoon, Seokhoon (Department of Electrical, Electronic and Computer Engineering, University of Ulsan)
  • Received : 2022.03.17
  • Accepted : 2022.03.24
  • Published : 2022.05.31
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Abstract

Workers' anomalous trajectories allow us to detect emergency situations in the workplace, such as accidents of workers, security threats, and fire. In this work, we develop a scheme to detect abnormal trajectories of workers using the edit distance on real sequence (EDR) and density method. Our anomaly detection scheme consists of two phases: offline phase and online phase. In the offline phase, we design a method to determine the algorithm parameters: distance threshold and density threshold using accumulated trajectories. In the online phase, an input trajectory is detected as normal or abnormal. To achieve this objective, neighbor density of the input trajectory is calculated using the distance threshold. Then, the input trajectory is marked as an anomaly if its density is less than the density threshold. We also evaluate performance of the proposed scheme based on the MIT Badge dataset in this work. The experimental results show that over 80 % of anomalous trajectories are detected with a precision of about 70 %, and F1-score achieves 74.68 %.

Keywords

Acknowledgement

This research was supported in part by Institute of Information & communication Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2020-0-00869, Development of 5G-based Shipbuilding & Marine Smart Communication Platform and Convergence Service), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) by the Ministry of Education under Grant 2021R1I1A3051364.

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