Earticle

초록

영어

Anomaly detection in human movements can improve safety in indoor workplaces. In this paper, we design a framework for detecting anomalous trajectories of humans in indoor spaces based on a variational autoencoder (VAE) with Bi-LSTM layers. First, the VAE is trained to capture the latent representation of normal trajectories. Then the abnormality of a new trajectory is checked using the trained VAE. In this step, the anomaly score of the trajectory is determined using the trajectory reconstruction error through the VAE. If the anomaly score exceeds a threshold, the trajectory is detected as an anomaly. To select the anomaly threshold, a new metric called D-score is proposed, which measures the difference between recall and precision. The anomaly threshold is selected according to the minimum value of the D-score on the validation set. The MIT Badge dataset, which is a real trajectory dataset of workers in indoor space, is used to evaluate the proposed framework. The experiment results show that our framework effectively identifies abnormal trajectories with 81.22% in terms of the F1-score.

목차

Abstract
1. Introduction
2. Background
2.1. Bi-LSTM
2.2. Variational Autoencoder
3. Methodology
3.1 Variational Autoencoder-based Anomaly Detection in Human Movements
3.2 Determine the Anomaly Threshold
4. Performance Evaluation
4.1 Dataset
4.2 Parameter Learning
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