• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.10-21
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• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.75-83
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• 2007

A model based approach of tracking the limbs of a walking human subject is proposed in this paper. The tracking process begins by building a data base composed of conditional probabilities of motions between the limbs of a walking subject. With a suitable amount of video footage from various human subjects included in the database, a probabilistic model characterizing the relationships between motions of limbs is developed. The motion tracking of a test subject begins with identifying and tracking limbs from the surveillance video image using the edge and silhouette detection methods. When occlusion occurs in any of the limbs being tracked, the approach uses the probabilistic motion model in conjunction with the minimum cost based edge and silhouette tracking model to determine the motion of the limb occluded in the image. The method has shown promising results of tracking occluded limbs in the validation tests.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.73-80
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• 2008

The recent trend in home network system includes intelligent home environments that remote monitoring and control service is achieved without restrictions by device types, time, and place. Also the use of a vacuum robot in homes is gradually generalized on account of the convenience of the use. In this paper, we proposed and realized new home-monitoring system with the employment of an self-movement robot as one trial for realizing an intelligent home under home network environment. The proposed system can freely monitor every where in home, because the system effectively overcame the surveillance limitations of the existing monitoring system by attaching a Wireless Router and WebCam to a commercial vacuum robot. The outdoor users of this system can readily monitor any place which they want to supervise by controlling a vacuum robot with mobile telecommunication devices such as PDA. The wireless router installed with Linux operation system "OpenWrt" made it possible for the system users to transmit images and to control a vacuum robot with RS-232 communication.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.129-129
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• 2010

적외선 검출소자(Infrared Photodetector)는 근적외선에서 원적외선 영역에 이르는 광범위한 파장 범위의 적외선을 이용하는 기기로서 대상물이 방사하는 적외선 영역의 에너지를 흡수하여 이를 영상화할 수 있는 장비이다. 적외선 관련 기술은 2차 세계대전 기간에 태동하였으며, 현재에는 원거리 감지기술 등과 접목되면서 그 활용 분야가 다양해지고 있다. 특히 능동형 정밀 타격무기를 비롯한 감시 정찰 장비 및 지능형 전투 장비 시스템 등에 대한 요구를 바탕으로 보다 정밀하고 신속한 표적 감지 및 정보처리 기술에 관한 연구가 선진국을 통해서 활발히 진행되고 있다. 기존의 Bolometer 형식의 열 감지 소자는 반응 속도가 느리고 측정 감도가 낮은 단점이 있으며, MCT(HgCdTe)를 이용한 적외선 검출기의 경우 높은 기계적 결함과 77K 저온에서 동작해야하기 때문에 발생하는 추가 비용 등이 문제점으로 지적되고 있다[1]. 이에 반해 화합물 반도체 자기조립 양자점(self-assembled quantum dot)을 이용한 적외선 수광소자는 양자점이 가지는 불연속적인 내부 에너지 준위로 인하여, 높은 내부 양자 효율과 온도 안정성을 기대할 수 있으며, 고성능, 고속처리, 저소비전력 및 저소음의 실현이 가능하다. 본 연구에서는 적층 InAs/InGaAs dot-in-a-well 구조를 유기금속화학기상증착법을 이용하여 성장하고 이를 소자에 응용하였다. 균일한 적층 양자점의 성장을 위해서 원자현미경(atomic force microscopy)을 이용하여, 각 층의 양자점의 크기와 밀도를 관찰하였고, photoluminescence (PL)를 이용하여 발광특성을 연구하였다. 각 층간의 GaAs space layer의 두께와 온도 조절 과정을 조절함으로써 균일한 적층 양자점 구조를 얻을 수 있었다. 이를 이용하여 양자점의 전도대 내부의 에너지 준위간 천이(intersubband transition)를 이용하는 n-type GaAs/intrinsic InAs 양자점/n-type GaAs 구조의 양자점 적외선수광소자 구조를 성장하였다. 이 과정에서 상부 n-type GaAs의 성장 온도가 600도 이상이 되는 경우 발광효율이 급격히 감소하고, 암전류가 크게 증가하는 것을 관찰하였다. 이는 InAs 양자점과 주변 GaAs 간의 열에 의한 상호 확산에 의하여 양자점의 전자 구속 효과를 저해하는 것으로 설명된다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.85-91
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• 2020

Recently, video-taping equipment such as CCTV have been seeing more use for crime prevention and general safety concerns. Since these video-taping equipment operates all throughout the day, the need for security personnel is lessened, and naturally costs incurred from managing such manpower should also decrease. However, technology currently used predominantly lacks self-sufficiency when given the task of searching for a specific object in the recorded video such as a person, and has to be done manually; current security-based video equipment is insufficient in an environment where real-time information retrieval is required. In this paper, we propose a technology that uses the latest deep-learning technology and OpenCV library to quickly search for a specific person in a video; the search is based on the clothing information that is inputted by the user and transmits the result in real time. We implemented our system to automatically recognize specific human objects in real time by using the YOLO library, whilst deep learning technology is used to classify human clothes into top/bottom clothes. Colors are also detected through the OpenCV library which are then all combined to identify the requested object. The system presented in this paper not only accurately and quickly recognizes a person object with a specific clothing, but also has a potential extensibility that can be used for other types of object recognition in a video surveillance system for various purposes.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.87-98
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• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1258-1268
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• 2005

While the concept of Home Network is laying by and its interests are increasing by means of digitalizing of the information communication infrastructure, many efforts are in progress toward convenient lives. Moreover, as information household appliances which have a junction of connecting to the network are appearing over the past a few years, the demands against intellectual Home Services are increasing. In this paper, by being based upon Multimedia which is an essential factor for developing of various application services on ubiquitous computing environments, we suggest a simplified application model that could apply the information to the automated processing system after studying user's behavior patterns using authentication and access control for identity certification of users. In addition, we compared captured video images in the fixed range by pixel unit through some time and checked disorder of them. And that made safe of user certification as adopting self-developed certification method which was used 'Hash' algorism through salt function of 12 byte. In order to show the usefulness of this proposed model, we did some testing by emulator for control of information after construction for Intellectual Multimedia Server, which ubiquitous network is available on as a scheme so as to check out developed applications. According to experimental results, it is very reasonable to believe that we could extend various multimedia applications in our daily lives.