• 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.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.24-32
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• 2010

In this paper we proposed an object tracking system that detects the abandoned and removed objects, which is to be used in the intelligent surveillance applications. After the GMM based background subtraction and by using histogram method, the static region is identified to detect abandoned and removed objects. Since the system is implemented on DSP chip, it operates in realtime and is programmable. The input videos used in the experiment contain various indoor and outdoor scenes, and they are categorized into three different complexities; low, midium and high. By 10 times of experiment, we obtained high detection ratio at low and medium complexity sequences. On the high complexity video, successful detection ratio was relatively low because the scene contains crowdedness and repeated occlusion. In the future work, these complicated situation should be solved.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.149-162
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• 2005

This paper suggest a way to detect a specific wanted figure in public places such as subway stations and banks by comparing color face images extracted from the real time CCTV with the face images of designated specific figures. Assuming that the characteristic of the surveillance camera allows the face information in screens to change arbitrarily and to contain information on numerous faces, the accurate detection of the face area was focused. To solve this problem, the normalization work using subsampling with $20{\times}20$ pixels on arbitrary face images, which is based on the Perceptron Neural Network model suggested by R. Rosenblatt, created the effect of recogning the whole face. The optimal linear filter and the histogram shaper technique were employed to minimize the outside interference such as lightings and light. The addition operation of the egg-shaped masks was added to the pre-treatment process to minimize unnecessary work. The images finished with the pre-treatment process were divided into three reception fields and the information on the specific location of eyes, nose, and mouths was determined through the neural network. Furthermore, the precision of results was improved by constructing the three single-set network system with different initial values in a row.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.812-820
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• 2012

In this paper, multiple pedestrians tracking system using Histogram of Oriented Gradient and occlusion detection is proposed. The proposed system is applicable to Intelligent Surveillance System. First, we detect pedestrian in a image sequence using pedestrian's feature. To get pedestrian's feature, we make block-histogram using gradient's direction histogram based on HOG(Histogram of Oriented Gradient), after that a pedestrian region is classified by using Linear-SVM(Support Vector Machine) training. Next, moving objects are tracked by using position information of the classified pedestrians. And we create motion trajectory descriptor which is used for content based event retrieval. The experimental results show that the proposed method is more fast, accurate and effective than conventional methods.

• 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.

• 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 the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.80-87
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• 2011

In this paper we present subject region-based auto-focusing algorithm using noise robust focus measure. The proposed algorithm automatically estimates the main subject using entropy and solves the traditional problems with a subject position or high frequency component of background image. We also propose a new focus measure by analyzing the discrete cosine transform coefficients. Experimental results show that the proposed method is more robust to Gaussian and impulse noises than the traditional methods. The proposed algorithm can be applied to Pan-tilt-zoom (PTZ) cameras in the intelligent video surveillance system.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.713-725
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• 2019

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.