Earticle

초록

영어

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 %.

목차

Abstract
1. Introduction
2. Definitions
2.1. Definitions Related to Trajectory
2.2. Definition of EDR
3. Methods
3.1 Anomalous Trajectory Detection Framework
3.2 Determining Distance Threshold
3.3 Determining Density Threshold
4. Performance Evaluation
4.1 Dataset
4.2 Experiment Setup
4.3 Results
5. Conclusion
Acknowledgement
References

키워드

저자

• Doi Thi Lan [ Ph.D. Student, Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Korea ]
• Seokhoon Yoon [ Professor, Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380