• KIISE Transactions on Computing Practices
• /
• v.21 no.8
• /
• pp.549-554
• /
• 2015

As intuitive hardware interfaces continue to be developed, it has become more important to recognize the posture of the user. An efficient alternative to adding expensive sensors is to implement computer vision systems. This paper proposes a method to recognize a user's postured in a live broadcast bi-directional participatory TV program. The proposed method first estimates the position of the user's hands by generation a facial color map for the user and a motion map. The posture is then recognized by computing the relative position of the face and the hands. This method exhibited 90% accuracy in an experiment to recognize three defined postures during the live broadcast bi-directional participatory TV program, even when the input images contained a complex background.

• Journal of Korea Multimedia Society
• /
• v.14 no.5
• /
• pp.607-613
• /
• 2011

In this paper, we propose a hand image segmentation method using the dynamic threshold values on input images with various lighting and background attributes. First, a moving hand silhouette is extracted using the camera input difference images, Next, based on the R,G,B histogram analysis of the extracted hand silhouette area, the threshold interval for each R, G, and B is calculated on run-time. Finally, the hand area is segmented using the thresholding and then a morphology operation, a connected component analysis and a flood-fill operation are performed for the noise removal. Experimental results on various input images showed that our hand segmentation method provides high level of accuracy and relatively fast stable results without the need of the fixed threshold values. Proposed methods can be used in the user interface of mixed reality applications.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.135-142
• /
• 2019

In this paper, we propose a system that can identify the position of naval ship personnel at a glance by utilizing the Bluetooth-based beacons. The system proposed in this paper, installs a beacon receiver which are short-range wireless communication devices for each cabin, and in the installed beacon receiver receives information from beacons held by personnel. The received information is transmitted to the processing server, and the processing server transmits the integrated information of the cabin to the display module. Display module displays personnel information located in each cabin. As a result of simulations using the designed system, it was confirmed that the integrated information is transferred to the display module and displayed. Unlike existing situations where personnel positions are reported orally within the ship, the system can quickly and in real time determine the position of personnel, allowing for the management of personnel in non-combat situations and the rapid battle disposition in combat situations. This is expected to contribute greatly to the improvement of fighting power.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.6
• /
• pp.7-14
• /
• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.5
• /
• pp.637-644
• /
• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.5
• /
• pp.53-59
• /
• 2018

Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.

• Journal of Digital Convergence
• /
• v.19 no.3
• /
• pp.195-202
• /
• 2021

This study is a systematic review study to confirm the characteristics, effects, and application strategies of domestic and foreign virtual reality programs provided by dementia education. This study is based on the literature published up to July 2020, CINAHL, Cochrane, EMBASE, and Pubmed electronic databases were used overseas, and RISS, KISS, and DBpia were searched in Korea. Based on the selection criteria and exclusion criteria, the final six papers were used for analysis. As a result of the analysis, the subjects of the selected literature were two informal caregivers (33.3%), one general (16.7%), and three students (50.0%) including nursing students and medical and pharmacy students. The virtual reality program was effective in a total of five areas(empathy ability, dementia attitude including knowledge and comfort of dementia, trust in own abilities, dyadic relationship scale), of which 75% of the studies measured empathy ability had significant effects. The results of this study confirm that virtual reality technology can be used for human-centered nursing based on empathy.

• Journal of Aerospace System Engineering
• /
• v.16 no.4
• /
• pp.109-115
• /
• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.63-70
• /
• 2023

Due to the spread of high-definition black boxes and the introduction of mobile applications such as 'Smart Citizens Report' and 'Safety Report', the number of public interest reports for violations of Traffic Law has increased rapidly, resulting in shortage of police personnel to handle them. In this paper, we describe the development of a system that can automatically detect lane violations which account for the largest proportion of public interest reporting videos for violations of traffic laws, using deep learning algorithms. In this study, a method for recognizing a vehicle and a solid line object using a YOLO model and a Lanenet model, a method for tracking an object individually using a deep sort algorithm, and a method for detecting lane change violations by recognizing the overlapping range of a vehicle object's bounding box and a solid line object are described. Using this system, it is expected that the shortage of police personnel in charge will be resolved.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.1
• /
• pp.191-203
• /
• 2009

To provide dynamic services for users in ubiquitous computing environments by considering context in real-time, in our previous work we proposed Metaservice concept, the description specification and the process for building a Metaservice library. However, our previous process generates separated models - UML, OWL, OWL-S based models - from each step, so it did not provide the established method for translation between models. Moreover, it premises aid of experts in various ontology languages, ontology editing tools and the proposed Metaservice specification. In this paper, we design the translation process from domain ontology in OWL to Metaservice Library in OWL-S and develop a visual tool in order to enable non-experts to generate consistent models and to construct a Metaservice library. The purpose of the Metaservice Library translation process is to maintain consistency in all models and to automatically generate OWL-S code for Metaservice library by integrating existing OWL model and Metaservice model.