• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1511-1518
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• 2007

As studies on ubiquitous computing are actively conducted, desire for various services, including image transmission storage, search and remote monitoring. has been expanding into mobile environment as well as to PCs. while CCTV (closed circuit TV) and un DVR (Digital video Recording) are used in places where security service such as intrusion detection system is required, these are high-end equipment. So it is not easy for ordinary users or household and small-sized companies to use them. Besides, they are difficult to be carried and camera solution for mobile device does not support high-quality function and provides low-definition of QVGA for picture quality. Therefore, in this study, design and implementation of embedded system of high-definition image transmission for ubiquitous mobile device which is not inferior to PC or DVR are described. To this end, usage of dedicated CPU for mobile device and design and implementation of MPEG-4 H/W CODEC also are examined. The implemented system showed excellent performance in mobile environment, in terms of speed, picture quality.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.145-151
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• 2013

This research is to suggest a method for investigating car number plates among the information managed in parking facilities. The investigation of car number plate is generally used to know how long vehicles are parked. Also, it can provide the information about the parking turnover rate and the mean parking duration of parked vehicles. This research performs the investigation using cameras at a distance of time. That is, the given distance of time from cameras is assigned to each parked vehicle, and then it can find the mean parking time of parked vehicles. Also, it can check the parking turnover rate of parked vehicles at a space unit of parking lot in an hour. The information such as the mean parked duration and the parking turnover rate of parked vehicles taken from this method is helpful to find and understand the inefficient use of parking facilities. With this suggested method, this research attempted to check the mean parking duration and the parking turnover rate of parked vehicles.

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

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.117-123
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• 2023

In the fintech environment, Smart phones are mainly used for various service. User authentication technology is required to use safe services. Authentication is performed by transmitting authentication information to the server when the PIN or password is entered and touch the button completing authentication. But A post-attack is possible because the smudge which is the trace of using screen remains instead of recording attack with a camera or SSA(Shoulder Surfing Attack). To prevent smudge attacks, users must erase their fingerprints after authentication. In this study, we proposed a technique to determine whether to erase fingerprints. The proposed method performed erasing fingerprint which is the trace of touching after entering PIN and designed the security keypads that processes instead of entering completion button automatically when determined whether the fingerprint has been erased or not. This method suggests action that must erase the fingerprint when entering password. By this method, A user must erase the fingerprint to complete service request and can block smudge attack.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.38-45
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• 2022

Due to the development of fintech technology, financial transactions using smart phones are being activated. The password for user authentication during financial transactions is entered through the virtual keypad displayed on the screen of the smart phone. When the password is entered, the attacker can find out the password by capturing it with a high-resolution camera or spying over the shoulder. A virtual keypad with security applied to prevent such an attack is difficult to input on a small touch-screen, and there is still a vulnerability in peeping attacks. In this paper, the entire keypad is divided into several groups and displayed on a small screen, touching the group to which the character to be input belongs, and then touching the corresponding character within the group. The proposed method selects the group to which the character to be input belongs, and displays the keypad in the group on a small screen with no more than 10 keypads, so that the size of the keypad can be enlarged more than twice compared to the existing method, and the location is randomly placed, hence location of the touch attacks can be blocked.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.103-108
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• 2024

In this paper, a smart elevator system was studied using real-time object detection technology based on YOLO(You only look once)v5. When an external elevator button is pressed, the YOLOv5 model analyzes the camera video to determine whether there are people waiting, and if it determines that there are no people waiting, the button is automatically canceled. The study introduces an effective method of implementing object detection and communication technology through YOLOv5 and MQTT (Message Queuing Telemetry Transport) used in the Internet of Things. And using this, we implemented a smart elevator system that determines in real time whether there are people waiting. The proposed system can play the role of CCTV (closed-circuit television) while reducing unnecessary power consumption. Therefore, the proposed smart elevator system is expected to contribute to safety and security issues.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.77-89
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• 2024

This paper presents an unmanned store management system that can provide inventory management and theft prevention for displayed products using a small camera that can monitor the shelves of sold products in small and medium-sized stores. This system is a service solution that integrates object recognition, real-time communication, security management, access management, and mobile authentication. The proposed system uses a custom YOLOv5-x model to recognize objects on the display, measure quantities in real time, and support real-time data communication with servers through Raspberry Pie. In addition, the number of objects in the database and the object recognition results are compared to detect suspected theft situations and provide burial images at the time of theft. The proposed unmanned store solution is expected to improve the efficiency of small and medium-sized unmanned store operations and contribute to responding to theft.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.275-284
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• 2008

With increases in recent security requirements, biometric technology such as fingerprints, faces and iris recognitions have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins in order to identify individuals at a high level of accuracy. This paper proposes new device and methods for touchless finger vein recognition. This research presents the following five advantages compared to previous works. First, by using a minimal guiding structure for the finger tip, side and the back of finger, we were able to obtain touchless finger vein images without causing much inconvenience to user. Second, by using a hot mirror, which was slanted at the angle of 45 degrees in front of the camera, we were able to reduce the depth of the capturing device. Consequently, it would be possible to use the device in many applications having size limitations such as mobile phones. Third, we used the holistic texture information of the finger veins based on a LBP (Local Binary Pattern) without needing to extract accurate finger vein regions. By using this method, we were able to reduce the effect of non-uniform illumination including shaded and highly saturated areas. Fourth, we enhanced recognition performance by excluding non-finger vein regions. Fifth, when matching the extracted finger vein code with the enrolled one, by using the bit-shift in both the horizontal and vertical directions, we could reduce the authentic variations caused by the translation and rotation of finger. Experimental results showed that the EER (Equal Error Rate) was 0.07423% and the total processing time was 91.4ms.