• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.63-74
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• 2020

This study investigates the behavior of foreign investors in the Stock Exchange of Thailand (SET) in the time of coronavirus disease 2019 (COVID-19) as to whether trading is abnormal, what strategy is followed, whether herd behavior is present, and whether the actions destabilize the market. Foreign investors' trading behavior is measured by net buying volume divided by market capitalization, whereas the stock market behavior is measured by logged return on the SET index portfolio. The data are daily from Tuesday, August 28, 2018, to Monday, May 18, 2020. The study extends the conditional-regression model in an event-study framework and extracts the unobserved abnormal trading behavior using the Kalman filtering technique. It then applies vector autoregressions and impulse responses to test for the investors' chosen strategy, herd behavior, and market destabilization. The results show that foreign investors' abnormal trading volume is negative and significant. An analysis of the abnormal trading volume with stock returns reveals that foreign investors are not positive-feedback investors, but rather, they self-herd. Although foreign investors' abnormal trading does not destabilize the market, it induces stock-return volatility of a similar size to normal trade. The methodology is new; the findings are useful for researchers, local authorities, and investors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.92-98
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• 2015

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring (SHM) technique is increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and influenced by various external loads. "Abnormal behavior point" is a moment when the structure starts vibrating abnormally and this can be detected by comparing between before and after abnormal behavior point. In other words, anomalous behavior is a sign of damage on structures and estimating the abnormal behavior point can be directly related to the safety of structure. Abnormal behavior causes damage on structures and this leads to enormous economic damage as well as damage for humans. This study proposes an estimating technique to find abnormal behavior point using Hilber-Huang Transform which is a time-frequency signal analysis technique and the proposed algorithm has been examined through laboratory tests with a bridge model using a shaking table.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.399-414
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• 2022

The purpose of this study was to test the structural model for the effect on abnormal eating behavior targeting 493 nurses. Results, The direct effects of variables affecting abnormal eating behavior were in the order of eating abstinence and socially imposed perfectionism, and these variables explained 85% of abnormal eating behavior. Explicit narcissism had a significant effect on abnormal eating behavior through socially imposed perfectionism and eating restraint, and sociocultural attitude toward appearance through eating restraint. In the multi-group moderating effect, the path coefficients between job stress and abnormal eating behavior, socially imposed perfectionism and abnormal eating behavior were different between groups. Therefore, it is necessary to find a way to lower the socially-imposed perfectionism and nursing intervention that can escape excessive eating abstinence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4553-4562
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• 2010

With developing networks, information security is going to be important and therefore lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.431-433
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• 2021

In this paper, abnormal behavior monitoring system using YOLO AI platform was implemented and had superior response characteristics compared to the conventional monitoring system using two-shot detection by using one-shot detection of YOLO system. The YOLO platform was trained using image dataset composed of abnormal behaviors such as assault, theft, and arson. The abnormal behavior monitoring system consists of client and server and can be applicable to smart cities to solve various crime problems if it is commercialized.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Smart Media Journal
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• v.6 no.3
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• pp.95-102
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• 2017

A large volume of research has been devoted to the development of security tools for protecting the Smart Grid systems, however the most of them have not taken the Availability, Integrity, Confidentiality (AIC) security triad model, not like CIA triad model in traditional Information Technology (IT) systems, into account the security measures for the electricity control systems. Thus, this study would propose a novel security framework, an abnormal behavior detection system, to maximize the availability of the control systems by considering a unique set of characteristics of the systems.