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Detecting Anomalous Trajectories of Workers using Density Method
DOI THI LAN,윤석훈 한국인터넷방송통신학회 2022 International Journal of Internet, Broadcasting an Vol.14 No.2
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 %.
Detecting Abnormal Human Movements Based on Variational Autoencoder
DOI THI LAN,윤석훈 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.3
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.
Duong, Dat Van Anh,Lan, Doi Thi,Yoon, Seokhoon The Institute of Internet 2022 International Journal of Internet, Broadcasting an Vol.14 No.4
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 clustering-based 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.
Estimating Workers’ Locations in Industrial Sites
Quan T. Ngo(오딴콴),Dat Van Anh Duong(즈엉 반 안 닷),Doi Thi Lan(도이 티 란),Seokhoon Yoon(윤석훈) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
Worker location estimation plays an important role in reducing the amount of time for rescue operations when an incident occurs, thus improves worker safety. In this paper, we address the problem of estimating the current location of a worker in the working sites, given his/her historical location records. A large-scale dataset of Wi-Fi traces is used to train and test the proposed model.