• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.675-682
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• 2004

With the five-day workweek system in bank and the increased usage of ATM(Automatic Toller Machine), it is required that the financial crime using stolen credit card should be prevented. Though a CCTV camera is usually installed in near ATM, an intelligent criminal can cheat it disguising himself with sunglass or mask. In this paper, we propose facial feature verification system which can detect whether the user's face can be Identified or not, using image processing algorithm and SVM(Support Vector Machine). Experimental results show that FAR(Error Rate for accepting a disguised man as a non-disguised one) is 1% and FRR(Error Rate for rejecting a normal/non-disguised man as a disguised one) is 2% for training data. In addition, it shows the FAR of 2.5% and the FRR of 1.43% for test data.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.143-148
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• 2016

There is increasing concern about security as a need for increased safety in the information industry society. However, it does not meet the needs for safety including CCTV. Therefore, in this paper, we link the processing technology using the image information to the IPS system consisting of GPS and Beacon. It designed a conventional RFID tag attached discomfort and image tracking system is limited to complement the disadvantages identifiable area. To this end, we designed a smart device and the Internet of Things convergence system and a research to ensure the accuracy and reliability of the IPS of the access control system. Finally, by leveraging intelligent video information using a PTZ camera, and set the entrant management policies it was carried out to control the situation and control. Also, by designing the integrated video tracking system, an authentication server, visualization systems were designed to establish an efficient technique for analyzing the IPS entrant behavior patterns.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.209-214
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• 2018

The subway, which is an urban railway, is the core of public transportation. Urban railways are always exposed to serious problems such as theft, crime and terrorism, as many passengers use them. Especially, due to the nature of urban railway environment, the scope of surveillance is widely dispersed and the range of surveillance target is rapidly increasing. Therefore, it is difficult to perform comprehensive management by passive surveillance like existing CCTV. In this paper, we propose the implementation, design method and object recognition algorithm for intelligent object recognition system in urban railway. The object recognition system that we propose is to analyze the camera images in the history and to recognize the situations where there are objects in the landing area and the waiting area that are not moving for more than a certain time. The proposed algorithm proved its effectiveness by showing detection rate of 100% for Selected area detection, 82% for detection in neglected object, and 94% for motionless object detection, compared with 84.62% object recognition rate using existing Kalman filter.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.64-76
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• 2008

To increase road safety at blackspots, it is needed to develop a new method that can process before accident occurrence. Accident situation could result from traffic conflict. Traffic conflict decision technique has an advantage that can acquire and analyze data in time and confined space that is less through investigation. Therefore, traffic conflict technique is highly expected to be used in many application of road safety. This study developed traffic conflict decision program that can analyze and process from signalized intersection image. Program consists of the following functional modules: an image input module that acquires images from the CCTV camera, a Save-to-Buffer module which stores the entered images by differentiating them into background images, current images, difference images, segmentation images, and a conflict detection module which displays the processed results. The program was developed using LabVIEW 8.5 (a graphic language) and the VISION module library.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.111-116
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• 2021

This study is a study on improving the pedestrian Care system, which delivers jaywalking events in real time to the autonomous driving control center and Autonomous driving vehicles in operation and issues warnings and announcements to pedestrians based on pedestrian signals. In order to secure reliability of object detection method of pedestrian Care system, the inspection method combined with camera sensor with Lidar sensor and the improved system algorithm were presented. In addition, for the occurrence events of Lidar sensors and intelligent CCTV received during the operation of autonomous driving vehicles, the system algorithm for the elimination of overlapping events and the improvement of accuracy of the same time, place, and object was presented.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.418-427
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• 2016

A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.196-204
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• 2017

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.75-84
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• 2003

Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, are costly and disruptive to install. The use of video cameras coupled with computer vision techniques offers an attractive alternative to current sensors. Video based traffic monitoring systems are now being considered key points of advanced traffic management systems. In this paper, we propose the new method which extract the traffic information using video camera. The proposed method uses an adaptive updating scheme for background in order to reduce the false alarm rate due to various noises in images. also, the proposed extraction method of traffic information calculates the traffic volume ratio of vehicles passing through predefined detection area, which is defined by the length of profile occupied by cars over that of overall detection area. Then the ratio is used to define 8 different states of traffic and to interpret the state of vehicle flows. The proposed method is verified by an experiment using CCTV traffic data from urban area.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.114-123
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• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.

• 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.