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YOLOv5 based Anomaly Detection for Subway Safety Management Using Dilated Convolution
Nusrat Jahan Tahira,박주령,임승진,박장식 한국산업융합학회 2023 한국산업융합학회 논문집 Vol.26 No.2
With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniques, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.
Performance Comparison of Human Activity Recognition for Unmanned Retails
Sheilla Wesonga,Nusrat Jahan Tahira,Jang-Sik Park 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Lately, the broad usage of technology in almost all aspects of life has led to the increase in research supporting technology advancement. One of these research topics is Human Activity Recognition (HAR) with diverse applicability which include and not limited to video surveillance, healthcare and education. In this paper, we present a study based on human activity recognition while employing the Kinect RGB and Depth sensor camera to recognize seven different human activities (7 classes). The joint angles extracted from the Kinect depth sensor each has 3 axes (X, Y, Z) for the 8 limbs employed in our experiment as the feature vectors. For the purpose of classifying the human activities, we train and test with 3 different state of the art recurrent neural network models (GRU, LSTM, Bi-LSTM). The comparison of the 3 recurrent neural network models shows that LSTM has a higher human activity classification accuracy at 96% and using the confusion matrix as the performance metric for all the models, we show classification per activity.