• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.652-654
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• 2012

본 논문에서는 다중 심도 카메라 기반의 실시간 피플 카운팅 시스템을 제안 한다. 카메라 영상으로부터 사람을 감지하고 추적하는 시스템 및 그 방법에 관한 것으로, 피플 카운팅 시스템은 쇼핑몰이나 대형건물의 출입구 등과 같은 다양한 환경에 적용될 수 있다. 기존 피플 카운팅 시스템에서의 급격한 조명의 변화나 겹침 현상, 가림 현상에 대한 해결 방법으로, 다중 심도 카메라 환경에서 동일 객체 추적을 위해 RLM(Range Laser Method)를 적용하고, 조명 등 환경 변화에 강인한 배경 제거 및 물체 검출 기법으로 가우시안 혼합 모델(Gaussian Mixture Model)을 적용해 객체인식에 대한 정확도를 높인다. 또한, 객체를 블랍(Blob)으로 지정해 확장 칼만 필터(Extended Kalman Filter, EKF) 방법으로 객체를 추적한다. 본 제안은 피플 카운팅 시스템에의 객체 검출 및 인식에 대한 정확도를 향상시킬 수 있으리라 기대된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.4
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• pp.348-359
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• 1989

The multitree pattern recognition algorithm proposed by [1] and [2] is modified in order to improve its performance. The basic idea of the multitree pattern classification algorithm is that the binary dceision tree used to classify an unknow pattern is constructed for each feature and that at each stage, classification rule decides whether to classify the unknown pattern or to extract the feature value according to the feature ordet. So the feature ordering needed in the calssification procedure is simple and the number of features used in the classification procedure is small compared with other classification algorithms. Thus the algorithm can be easily applied to real pattern recognition problems even when the number of features and that of the classes are very large. In this paper, the wighting factor assignment scheme in the decision procedure is modified and various classification rules are proposed by means of the weighting factor. And the branch and bound method is applied to feature subset selection and feature ordering. Several experimental results show that the performance of the multitree pattern classification algorithm is improved by the proposed scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.111-113
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• 2012

KINECT is a gesture recognition camera produced by Microsoft Corp. KINECT SDK are widely available and many applications are actively being developed. Especially, KIET (Kinect Image Extraction Technique) has been used mainly for extracting moving objects from the input image. However, KIET has difficulty in extracting the human head due to the absorption of light. In order to overcome this problem, this paper proposes a new method for improving the KIET performance by using both color-image and depth image. Through experimental results, it is shown that the proposed method performs better than the conventional KIET algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.537-542
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• 2021

This paper improves the possibility of recognizing fast-moving objects through the YOLO (You Only Look Once) deep learning recognition model in an application environment for object recognition in images. The purpose was to study the method of collecting semantic data through processing. In the recognition model, the moving object recognition error was identified as unrecognized because of the difference between the frame rate of the camera and the moving speed of the object and a misrecognition due to the existence of a similar object in an environment adjacent to the object. To minimize the recognition errors by compensating for errors, such as unrecognized and misrecognized objects through the proposed data collection method, and applying vision processing technology for the causes of errors that may occur in images acquired for sports (tennis games) that can represent real similar environments. The effectiveness of effective secondary data collection was improved by research on methods and processing structures. Therefore, by applying the data collection method proposed in this study, ordinary people can collect and manage data to improve their health and athletic performance in the sports and health industry through the simple shooting of a smart-phone camera.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1309-1315
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• 2014

The switch for a maglev vehicle should be designed such that the vehicle safely changes its track without touching the guiderail. In particular, a medium-to-low-speed EMS -type maglev train relies heavily on a U-type electromagnet where it generates levitation force and guidance force simultaneously. Therefore, it is necessary to evaluate the safety of the vehicle whenever it passes the switch, as it lacks active control of the guidance force. Furthermore, when the vehicle passes a segmented switch, which is a group of curves made up of connected lines with a small radius of curvature, it may come into mechanical contact with the guiderail owing to the excessive lateral displacement of the electromagnet. The goal of this study is to analyze the influence of a segmented switch on the safety of major design-related variables for achieving improved running safety. We propose a three-dimensional multibody dynamics model composed of two cars with one body. Using the proposed model, we perform a simulation of the lateral air gap, which is one of the measurements of the running safety of the vehicle when it passes the switch. The analyzed design variables are the length between short span girder, the articulation angle, the length between two centers of a fixed girder at its ends, and the number of girders. On the basis of the effects of the considered design variables, we establish an optimized design of a switch with improved safety.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6420-6426
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• 2013

The indoor areas for the commercial use of automatic monitoring systems of mobile robot localization improves the cognitive abilities and the needs of the environment with this emerging and existing mobile robot localization, and object recognition methods commonly around its great sensor are leveraged. On the other hand, there is a difficulty with a problem-solving self-location estimation in indoor mobile robots using only the sensors of the robot. Therefore, in this paper, a self-position estimation method for an enhanced and effective mobile robot is proposed using a marker and CCTV video that is already installed in the building. In particular, after recognizing a square mobile robot and the object from the input image, and the vertices were confirmed, the feature points of the marker were found, and marker recognition was then performed. First, a self-position estimation of the mobile robot was performed according to the relationship of the image marker and a coordinate transformation was performed. In particular, the estimation was converted to an absolute coordinate value based on CCTV information, such as robots and obstacles. The study results can be used to make a convenient self-position estimation of the robot in the indoor areas to verify the self-position estimation method of the mobile robot. In addition, experimental operation was performed based on the actual robot system.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.356-359
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• 2010

CCTV(closed-circuit television) 시스템은 현재 도로 교통감시, 아파트 방범, 차량 번호판 인식 등 많은 분야에서 사용되고 있다. 사용되는 여러 분야에서 실제 물체와 감시 영상의 오차를 줄이기 위해 좀 더 향상된 화질의 영상을 필요로 하고 있다. 디지털 카메라가 보편화 되고 또한 급속한 영상 압축 기술의 발전으로 인해 고화질의 영상을 지원하는 카메라가 빠르게 출시되고 있다. 현재 보편화된 영상 압축 기술 중 가장 뛰어난 성능을 발휘하는 H.264를 지원 하는 카메라를 사용한 CCTV 감시 시스템이 늘고 있는 것이 현실이다. 본 논문에서는 H.264를 이용하여 고화질의 영상을 지원하는 여러 카메라를 동시에 원격으로 감시할 수 있는 Client Viewer 를 구현 하였다. 구현한 Client Viewer 는 실시간 재생 기능 및 녹화 기능을 지원 하며 녹화된 영상을 검색하고 재생하는 DVR 기능도 지원한다.