• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.4
• /
• pp.580-585
• /
• 2010

In this paper, The object Tracking System is designed by SOPC based Nios II embedded processor and C2H compiler. And this system using single PTZ camera can effectively control IPs in the platform of SOPC based Nios II Embedded Processor and creating IP by C2H(C-To-Hardware) compiler for image-in/output, image-processing and devices of communication that can supply various monitoring information to network or serial. Accordingly, Special quality and processing speed of object tracking using high-quality algorism in the system is improved by hardware/software programming methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.2
• /
• pp.401-406
• /
• 2008

Fall event detection is one of the most common problems for elderly people, especially those living alone because falls result in serious injuries such as joint dislocations, fractures, severe head injuries or even death. In order to prevent falls or fall-related injuries, several previous methods based on video sensor showed low fall detection rates in recent years. To improve this problem and outperform the system performance, this paper presented a novel approach for fall event detection in the elderly using a subtraction between successive difference images and temporal templates in real time environment. The proposed algorithm obtained the successful detection rate of 96.43% and the low false positive rate of 3.125% even though the low-quality video sequences are obtained by a USB PC camera sensor. The experimental results have shown very promising performance in terms of high detection rate and low false positive rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.5
• /
• pp.251-256
• /
• 2021

Unlike general cameras, a high-speed camera capable of capturing a large number of frames per second can enable the advancement of some image processing technologies that have been limited so far. This paper proposes a method of removing undesirable noise from an high-speed input color image, and then detecting a human face from the noise-free image. In this paper, noise pixels included in the ultrafast input image are first removed by applying a bidirectional filter. Then, using RetinaFace, a region representing the person's personal information is robustly detected from the image where noise was removed. The experimental results show that the described algorithm removes noise from the input image and then robustly detects a human face using the generated model. The model-based face-detection method presented in this paper is expected to be used as basic technology for many practical application fields related to image processing and pattern recognition, such as indoor and outdoor building monitoring, door opening and closing management, and mobile biometric authentication.

• 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.

• Advances in Computational Design
• /
• v.4 no.2
• /
• pp.73-90
• /
• 2019

The growth in the number of single-member households is a critical issue worldwide, especially among the elderly. For those living alone, who may be unaware of their health status or routines that could improve their health, a continuous healthcare monitoring system could provide valuable feedback. Assessing the performance adequacy of activities of daily living (ADL) can serve as a measure of an individual's health status; previous research has focused on determining a person's daily activities and extracting the most frequently performed behavioral patterns using camera recordings or wearable sensing techniques. However, existing methods used to extract common patterns of an occupant's activities in the home fail to address the spatio-temporal dimensions of human activities simultaneously. Though multiple sequence alignment (MSA) offers some advantages - such as inherent containment of the spatio-temporal data in sequence format, and rapid identification of hidden patterns - MSA has rarely been used to extract in-home ADL routines. This research proposes a method to extract a household occupant's ADL routines from a cumulative spatio-temporal data log of occupancy collected using a non-intrusive method (i.e., a tomographic motion detection system). The findings from an occupant's 28-day spatio-temporal activity log demonstrate the capacity of the proposed approach to identify routine patterns of an occupant's daily activities and to reveal the order, duration, and frequency of routine activities. Routine ADL patterns identified from the proposed approach are expected to provide a basis for detecting/evaluating abrupt or gradual changes of an occupant's ADL patterns that result from a physical or mental disorder, and can offer valuable information for home automation applications by enabling the prediction of ADL patterns.

• The Journal of Korean Institute of Information Technology
• /
• v.16 no.11
• /
• pp.115-121
• /
• 2018

Fluorescent images using near-infrared light have no worry about radioactivity, and images can be checked in real time during surgery. Therefore experiments using fluorescent images for monitoring lymph node biopsy are actively under way. Fluorescent imaging equipment uses high heat-generating components such as LED and camera, thus uses water-cooling system as a stable heating suppression means. However in the fluorescent image equipment, the water cooling system takes a large volume which is a disadvantage in terms of miniaturization of the equipment. Even if the air cooling system is used for miniaturizing the equipment, heat generation is a problem. In this paper, we have experimented with the air cooling method using PWM control for the miniaturization of the equipment, and confirmed the constant quality of the fluorescent image and the suppression of the heat generation without any problems even when the equipment is used for a long time.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1229-1234
• /
• 2018

In this paper, a fire prevention system based on a broadband powerline communication (PLC) system is implemented and a demonstration experiment is carried out to prevent from or promptly dealing with possible fires within the nacelle of a wind turbine generator system (WTGS). For this purpose, an inductive coupler having satisfactory attenuation characteristic in the frequency region for high-speed PLC is also manufactured. It is confirmed that the implemented system can monitor the environmental change inside the nacelle in real time by transmitting various information obtained by the sensors such as temperature, flame, and smoke sensor installed in the nacelle and thermal image recorded by a thermal camera to the ground control center through the PLC system. Therefore, it is, considered that the implemented system will significantly improve the reliability of the fire monitoring and prevention system of the WTGS in conjunction with the existing safety system.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.6
• /
• pp.243-250
• /
• 2018

This paper designs and implements a smart care system for the senior citizen who lives alone. Recently, as the level of living has increased due to the rapid improvement of medicine, living standard and environment, the proportion of the elderly population is increasing. In addition, the proportion of the elderly living alone, which is increasing with the aging society, suggests that the provision of services such as the elder care system and emergency notification is becoming an important issue. However, since the existing emergency notification technology analyzes fixed CCTV images, it is difficult to monitor in the blind spot of CCTV and to move to a place where the camera is not installed. There is a problem that it can not be performed. Therefore, in this paper, we design and develop a smart care system that utilizes robot and object tracking technology that can move in real time to overcome these shortcomings. This enables real-time monitoring regardless of the location, and prompts for assistance in case of an emergency, so that it can provide convenience to cares and assistants.

• Journal of IKEEE
• /
• v.23 no.3
• /
• pp.777-784
• /
• 2019

A pre-fire awareness and automatic notification system are required because it is possible to minimize the damage if the fire situation is precisely detected after a fire occurs in a place where people are unusual or in a mountainous area. In this study, we developed a RaspberryPi-based fire recognition system using Faster-recurrent convolutional neural network (F-RCNN) and single shot multibox detector (SSD) and demonstrated a fire alarm system that works with power line communication. Image recognition was performed with a pie camera of RaspberryPi, and the detected fire image was transmitted to a monitoring PC through an inductive power line communication network. The frame rate per second (fps) for each learning model was 0.05 fps for Faster-RCNN and 1.4 fps for SSD. SSD was 28 times faster than F-RCNN.

• Journal of information and communication convergence engineering
• /
• v.17 no.1
• /
• pp.74-83
• /
• 2019

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.