• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.25-30
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• 2011

This paper present a method for abnormal traffic behavior, or trajectory, detection in static traffic surveillance camera with user-defined trajectories. The method computes the abnormality of moving object with a trajectory of the object and user-defined trajectories. Because of using user-define based information, the presented method have more accurate and faster performance than models need a learning about normal behaviors. The method also have adaptation process of assigned rule, so it can handle scene variation for more robust performance. The experimental results show that our method can detect abnormal traffic behaviors in various situation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.25-27
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• 2021

With the recent development of deep learning technology, computer vision-based AI technologies have been studied to analyze the abnormal behavior of objects in image information acquired through CCTV cameras. There are many cases where surveillance cameras are installed in dangerous areas or security areas for crime prevention and surveillance. For this reason, companies are conducting studies to determine major situations such as intrusion, roaming, falls, and assault in the surveillance camera environment. In this paper, we propose a real-time abnormal behavior analysis algorithm using object detection and tracking method.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.69-79
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• 2010

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12C
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• pp.744-752
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• 2011

In intelligent surveillance system, various methods for detecting abnormal behavior were proposed recently. However, most researches are not robust enough to be utilized for actual reality which often has occlusions because of assumption the researches have that individual objects can be tracked. This paper presents a novel method to detect abnormal behavior by analysing major motion of the scene for complex environment in which object tracking cannot work. First, we generate Visual Word and Visual Document from motion information extracted from input video and process them through LDA(Latent Dirichlet Allocation) algorithm which is one of document analysis technique to obtain major motion information(location, magnitude, direction, distribution) of the scene. Using acquired information, we compare similarity between motion appeared in input video and analysed major motion in order to detect motions which does not match to major motions as abnormal behavior.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3147-3153
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• 2016

Background: Many strategies are required for cervical cancer reduction e.g. provision of education cautious sexual behavior, HPV vaccination, and early detection of pre-invasive cervical lesions and invasive cancer. Basic health data for cervical cytology/ HPV DNA and associated factors are important to make an appropriate policy to fight against cervical cancer. Aims: To assess the prevalence of abnormal cervical cytology and/or HPV DNA and associated factors, including sexual behavior, among Bangkok Metropolitan women. Materials and Methods: Thai women, aged 25-to-65 years old, had lived in Bangkok for ${\geq}5$ years were invited into the study. Liquid-based cervical cytology and HPV DNA tests were performed. Personal data were collected. Main Outcomes Measures: Rates of abnormal cytology and/ or high-risk HPV (HR-HPV) and factors associated with abnormal test (s) were studied. Results: Abnormal cytology and positive HR-HPV were found in 6.3% (279/4442 women) and 6.7% (295/4428), respectively. The most common abnormal cytology was ASC-US (3.5%) while the most common HR-HPV genotype was HPV 16 (1.4%) followed by HPV 52 (1.0%), HPV 58 (0.9%), and HPV 18 and HPV 51 at equal frequency (0.7%). Both tests were abnormal in 1.6% (71/4428 women). Rates of HR-HPV detection were directly associated with severity of abnormal cytology: 5.4% among normal cytology and 13.0%, 30.8%, 40.0%, 39.5%, 56.3% and 100.0% among ASC-US, ASC-H, AGC-NOS, LSIL, HSIL, and SCC, respectively. Some 5% of women who had no HR-HPV had abnormal cytology, in which 0.3% had ${\geq}$ HSIL. Factors associated with abnormal cytology or HR-HPV were: age ${\leq}40$ years, education lower than (for cytology) or higher than bachelor for HR-HPV), history of sexual intercourse, and sexual partners ${\geq}2$. Conclusions: Rates for abnormal cytology and HR-HPV detection were 6.3% and 6.7% HR-HPV detection was directly associated with severity of abnormal cytology. Significant associated factors were age ${\leq}40$ years, lower education, history of sexual intercourse, and sexual partners ${\geq}2$.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.492-500
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• 2014

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.525-526
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• 2006

Calcium-hexaluminate phase $(CA_6)$ is known to be effective for the crack shielding due to the spinel block crystal structure. In this study, we focused to the control of $CA_6$ morphology for good damage tolerance behavior in alumina and zirconia/calcium-hexaluminate $(CA_6)$ composites. Calcium-hexaluminate $(CA_6)$ composites were prepared from zirconia, alumina and calcium carbornate powders. Calcium-hexaluminate $(CA_6)$ phase was obtained by the solid reaction through the formation of intermediate phase $(CA_2)$. $CA_6$ phase showed the column type abnormal grain grown behavior composed of small blocks. Due to the typical microstructure of $CA_6$, alumina and zirconia/calcium-hexaluminate composites provide a well controlled crack propagation behavior.

• Journal of the Korean Ceramic Society
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• v.32 no.2
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• pp.147-154
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• 1995

Abnormal grain growth behavior of BaTiO3 ceramics with controlling of particle size distribution of calcined powder was investigated. The particle size distribution was controlled by changing the calcining temperature or by using of classification and regrinding process. With broadening of the normallized size distribution in calcined powder, it showeda normal grain growth behavior in sintered body due to an increase of volume fraction of seed grain in the calcined powder. It was supposed that the seed grains could easily contact each other for the rapid grain growth during sintering process and resulted in fast switching-over from abnormal to normal grain growth stage.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.542-544
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• 2019

Changes in a person's health affect one's lifestyle and work activities. According to the World Health Organization (WHO), abnormal activity is growing faster in people aged 60 or more than any other age group in almost every country. This trend steadily continues and expected to increase further in the near future. Abnormal activity put these people at high risk of expected incidents since most of these people live alone. Human abnormal activity analysis is a challenging, useful and interesting problem among the researchers and its particularly crucial task in life and health care areas. In this paper, we discuss the problem of abnormal activities of old people lives alone at home. We propose Convolutional Neural Network (CNN) based model to detect the abnormal behaviors of elderlies by utilizing six simulated action data from daily life actions.