• Fashion & Textile Research Journal
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• v.20 no.6
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• pp.656-668
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• 2018

The main object of this study was to understand the body image and body management behavior associated with desiring a thinner and more muscular body. The present study examined whether the drives for thinness and muscularity occur concurrently for both male and female college students, and whether there are gender differences. Moreover, the effects of objectified body consciousness on drive for thinness and drive for muscularity were investigated. A self-administered survey was conducted and a total of 390 data were used for data analysis. Participants included 197 male college students and 193 female students. Results indicated that male students reported significantly lower drive for thinness and higher drive for muscularity than female students. However, the drives for thinness and muscularity were significantly correlated for both male and female college students, and they reported discrepancies between their BMI and self-perceived weight and muscle mass. Findings supported the significant effects of objected body consciousness on the drives for thinness and muscularity for both male and females. Sub-dimensions of objectified body consciousness had differential effects on drive for thinness and muscularity. Specifically, body surveillance and body shame significantly influenced male and female students' drive for thinness, while their control belief did not have significant effects on their drive for thinness. Additionally, body shame emerged as significant unique predictor of drive for muscularity. In terms of gender comparisons, while the effect of body shame was the strongest for the males, the effect of surveillance was as strong as that of body shame for the females.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1431-1438
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• 2018

In this paper, we proposed a river water level detection and tracking of the river or dams based on image processing system. In past, most of the water level detection system used various water sensors. Those water sensors works perfectly but have many drawbacks such as high cost and harsh weather. Water level monitoring system helps in forecasting early river disasters and maintenance of the water body area. However, the early river disaster warning system introduces many conflicting requirements. Surveillance camera based water level detection system depends on either the area of interest from the water body or on optical flow algorithm. This proposed system is focused on water scaling area of a river or dam to detect water level. After the detection of scale area from water body, the proposed algorithm will immediately focus on the digits available on that area. Using the numbers on the scale, water level of the river is predicted. This proposed system is successfully tested on different water bodies to detect the water level area and predicted the water level.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.259-266
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• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.4
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• pp.63-77
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• 2014

The purpose of this study was to investigate the effects of sociocultural attitudes toward appearance and objectified body consciousness on male consumer' appearance management behavior. The subjects were 648 males aged from 20 to 59 years old, and the questionnaire consisted of sociocultural attitude toward appearance, objectified body consciousness, appearance management behavior, and subject' demographic characteristics. The data were analyzed by descriptive statistics, Cronbach's ${\alpha}$, factor analysis, and regression analysis. The results were as follows. Three dimensions(appearance importance awareness, slimness importance awareness, and internalization) were emerged on sociocultural attitude toward appearance. Three dimensions(body shame, body surveillance, and control belief) were emerged on objectified body consciousness. Five dimensions(skin, hair, body, fashion, and plastic surgery management) were emerged on appearance management behavior. In sociocultural attitude toward appearance dimensions, appearance importance awareness and internalization had important effects on appearance management behavior. In objectified body consciousness dimensions, body shame and control belief had important effects on appearance management behavior. This results concluded that sociocultural attitude toward appearance and objectified body consciousness are important variables to understand on male consumer' appearance management behavior.

• Journal of the Korea Fashion and Costume Design Association
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• v.19 no.3
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• pp.127-142
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• 2017

The purpose of this study was to investigate the clothing and cosmetics purchasing behaviors of middle aged male consumers according to objectified body consciousness. The subjects were 329 male adults aged from 40 to 59, and measuring instruments consisted of objectified body consciousness, clothing and cosmetics purchasing behaviors, and subjects' demographics attributions. The data were analyzed by factor analysis, cluster analysis, multiple response analysis, cross tabs analysis, and $x^2$ test using the SPSS program. The results were as follows. First, three factors (body shame, body surveillance, and control belief) emerged on objectified body consciousness. Second, subjects were divided into 2 groups (objectified group and non-objectified group) by objectified body consciousness. Third, these two consumer groups showed many differences regarding clothing and cosmetics purchasing behaviors. The objectified group showed many more positive clothing and cosmetics purchasing behaviors than the non-objectified group in terms of purchase motives, selection criteria, information source, purchase place, and purchase cost per month. These results show that objectified body consciousness is a useful variable for understanding adult male clothing and cosmetics purchasing behavior and to segment the male consumer market effectively.

• The Research Journal of the Costume Culture
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• v.22 no.5
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• pp.809-822
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• 2014

The purpose of this study was to investigate male adult' appearance management behavior according to objectified body consciousness. The subjects were 648 male adults aged from 20 to 59 and measuring instruments consisted of objectified body consciousness, appearance management behavior, and subjects' demographics attributions. The data were analyzed by Cronbach's ${\alpha}$, factor analysis, cluster analysis, multiple response analysis, cross tabs analysis, ${\chi}^2$ test, and t-test. The results were as follows. First, 3 dimensions (body shame, body surveillance, and control belief) were emerged on objectified body consciousness, and subjects were divided into 2 groups (objectified group, and non-objectified group) by this variable. Second, male adults were deeply aware of the need of appearance management, and showed the high level of intention to perform appearance management behavior. Third, objectified group showed much more active appearance management behavior than non-objectified group. This results concluded that objectified body consciousness is a very useful variable to understand male adult' appearance management behavior.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.85-92
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• 2017

Recently, in the field of video surveillance, a Deep Learning based learning method has been applied to a method of detecting a moving person in a video and analyzing the behavior of a detected person. The human activity recognition, which is one of the fields this intelligent image analysis technology, detects the object and goes through the process of detecting the body keypoint to recognize the behavior of the detected object. In this paper, we propose a method for Body Keypoint Localization based on Object Detection using RGB-D information. First, the moving object is segmented and detected from the background using color information and depth information generated by the two cameras. The input image generated by rescaling the detected object region using RGB-D information is applied to Convolutional Pose Machines for one person's pose estimation. CPM are used to generate Belief Maps for 14 body parts per person and to detect body keypoints based on Belief Maps. This method provides an accurate region for objects to detect keypoints an can be extended from single Body Keypoint Localization to multiple Body Keypoint Localization through the integration of individual Body Keypoint Localization. In the future, it is possible to generate a model for human pose estimation using the detected keypoints and contribute to the field of human activity recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.661-664
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• 2009

Recently, intelligent surveillance camera systems are needed popularly. However, current researches are focussed on improvement of a single module rather than implementation of an integrated system. In this paper, we implemented a moving wireless surveillance camera system which is composed of face detection, and using motion sensor. In our implementation, we used a camera module from SHARP, a pair of wireless video transmission module from ECOM, body of moving robot used for A4WD1 Combo kit for RC, a pair of ZigBee RF wireless transmission module from ROBOBLOCK, and a motion sensor module (AMN14111) from PANASONIC. We used OpenCV library for face dection and MFC for implement software. We identified real-time operations of face detection, PTT control, and motion sensor detecton. Thus, the implemented system will be useful for the applications of remote control, human detection, and using motion sensor.

• Proceedings of the ESK Conference
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• 1998.04a
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• pp.31-35
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• 1998

Many reaearchers have reproted that poor body postures are associated with pains or symptoms of musculoskeletal dissoders. Therefore, the ergonomic evaluation of postural stresses as well as biomechanical stresses is important when a job such as automobile assembly tasks involves highly repetitive and/or prolonged poor body postures. A macropostural classification shema was developed to characterise various body postures occurring in automobile assembly tasks in the study. To specify a postural code and stress level to each body posture, perceived joint discomforts were subjectively evaluated in the lab experiments for the full range of motion in five human body joints. Based on the reaults, a postural classification scheme was developed where the full range of motion in each body joint was classified into several codes repressenting different stress levels. The automobile tasks were clustered into 12 types based on the result walk-in-surveillance and the possible posture codes for each task type are defined. I was exposed that the poor postural problems in automobile assembly tasks were concerned in most part with arms, trunk and neck. Application of te developed schema to seven operations in automobile assembly tasks showed that the schema can be used as a tool to identify the operations and tasks involving highly stressful body postures. The schema can also be utilised as a basis to prioritise the candidate assembly operations for redesign of work methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.