• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1233-1242
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• 2017

Various attempts have been made to prevent crime risk. One of the cases where outdoor pedestrians are attacked by criminals is the abnormal health condition. When a mental or mental condition that can not sustain normal walking due to drunkenness is exposed, the case of being a crime is revealed through crime case analysis. In this study, we propose a method for estimating the state of an individual that can be detected in outdoor activities. In order to avoid the inconvenience of installing a separate terminal for event information transmission of sensors and sensors, it is possible to estimate an abnormal state by using a 3-axis acceleration sensor built in a smart phone. The state of the user can be estimated by analyzing the momentum of the user and analyzing it with the passage of time. It is possible to distinguish the flow of time at regular intervals, to recognize the activity patterns in each time band, and to distinguish between normal and abnormal. In this study, we have evaluated the total amount of kinetic energy and kinetic energy in each direction of the acceleration sensor and the Fourier transformed value of the total energy amount to distinguish the abnormal state.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1325-1334
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• 2016

The video incident detection System is a probe for rapid detecting the walker, falling, stopped, backwards, smoke situation in tunnel. Recently, the importance is increases from the downtown double deck tunnel in great depth underground space[1], but the legal basis is weak and the vulnerable situation experimental data. So, In this paper, we introduce a long-term log data analysis information in the tunnenl video incident detection system installed and experimental results in order to verify the feasibility of apply to video incident detection system for the double deck tunnel. It is proposed a few things about derives the problem of existing video incident detection system, improvements and reflect attributes for double deck tunnel. The contents described in this paper will contribute to refine the prototype of video incident detection system will apply to future double deck multi-layer tunnels.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.5
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• pp.35-49
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• 2014

This paper proposes the on-road object detection and classification algorithm by using a detection system consisting of only laser scanners. Each sensor data acquired by the laser scanner is fused with a grid map and the measurement error and spot spaces are corrected using a labeling method and dilation operation. Fuzzy method which uses the object information (length, width) as input parameters can classify the objects such as a pedestrian, bicycle and vehicle. In this way, the accuracy of the detection system is increased. Through experiments for some scenarios in the real road environment, the performance of the proposed detection and classification system for the actual objects is demonstrated through the comparison with the actual information acquired by GPS-RTK.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.510-512
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• 2015

최근 IT 기술의 발전으로 가장 보편화 되고 있는 첨단기기는 스마트폰이다. 길을 걸으며 스마트폰을 사용하는 사람들이 늘어나고 이러한 행위는 보행자 자신의 안전에도 크게 위협적인 요소가 될 수도 있지만 자칫 주변 사람들에게까지 돌이킬 수 없는 결과를 초래할 수도 있다. 본 논문에서는 사람들의 안전한 생활을 위한 지능형 신발을 설계한다. 설계한 신발은 사람들에게 편리하고 유용한 장애물 감지 및 알림 시스템을 설계하기 위해 초음파 센서와 진동 센서 그리고 LED를 이용한다. 또한 모바일 어플리케이션(Mobile application)을 개발하여 활용도를 높인다. 초음파 센서는 마이크로 프로세서에서 장애물과의 거리를 계산한다. 진동 센서는 초음파 센서에서 계산한 거리가 특정거리(IM-4M) 이내이면 신호를 받아 진동 센서 혹은 LED가 동작해 사람에게 장애물이 있다고 알려준다. 모바일 어플리케이션(Mobile application)은 신발이 스마트폰과 일정거리 이상 멀어져 블루투스(Bluetooth)가 끊기면 알림을 해줌으로써 분실 방지를 해주고. 신발의 각 기능들을 제어하도록 해준다. 또 장애물과의 거리가 얼마나 남았는지를 확인할 수 있는 기능도 있다. 따라서 본 논문에서 제안하는 장애물 감지 및 알림 기능은 사람들에게 안전을 보장해주고 저비용으로 구현 가능한 장점이 있다.

• Journal of IKEEE
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• v.18 no.1
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• pp.106-113
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• 2014

A pedestrian detector can be applied for various purposes such as monitoring or counting the number of people in some place, or detecting the people plunging in the driveway. There was a lot of related research. But, the detection speed is slow in embedded system because of the limited computing power. An algorithm for fast pedestrian detector using HOG in ARM SIMD architecture is presented in this paper. There is a way to quickly remove the background of image and to improve the detection speed using NEON parallel technique. When we tested with INRIA Person Dataset, the proposed pedestrian detector improves the speed by 3.01 times than previous one.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.133-136
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• 2023

많은 사람이 "다음 생에 성적 소수자나 이주민, 장애인으로 태어나고 싶니?"라는 질문을 받는다면 대부분 사람은 선뜻 "그렇다"라고 대답하기 어렵다. 그 근본적인 이유는 질문 속 사회적 약자들은 사회에서 다양한 불평등을 마주하고 있다는 의미일 것이다. 본 논문에서는 여러 소수자 중 시각장애인을 위한 보행 보조 기구를 제작하였다. 시각장애인들이 사용하는 지팡이는 지면의 장애물 파악에 도움은 되지만 공중에 떠 있는 장애물을 파악하고 피하기 어렵다. 이러한 단점을 보완하고자 라이다 센서를 이용하여 장애물 감지가 가능한 서비스를 시각장애인에게 제공한다. 라이다 센서는 레이저 광원을 방출하여 목표물에서 튕겨 되돌아오는 특성을 이용하여 사용자에게 전방에 장애물이 감지되면 시각장애인에게 TTS로 경고음을 제공한다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.156-157
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• 2018

본 논문에서 제안하는 기술은 무인기 획득 영상에 dense optical flow 기술을 적용하여, 이미지 내에서 급격히 움직이는 사물을 추출하는 기술이다. 제안 기술을 활용하여 사람, 사물 장소에 해당하는 시간에 따른 데이터인 다중로그 데이터로 융합 분석하여 낙석, 산사태, 비탈면 붕괴등을 감지 할 수 있게 되어 보행자의 안전을 보장 하고자 한다. 본 논문에서는 해당 기술을 구현하기 위한 무인기 및 탑재 장비와 데이터 처리를 위한 서버들간의 인터페이스 및 분석 알고리즘을 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1544-1546
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• 2013

도시철도는 대표적인 대중교통으로써, 하루에도 수백만명의 승객들이 이용하고 있다. 따라서 도시철도를 이용하는 승객들의 안전이 보장되어야 하며, 안전한 서비스 제공 및 예방 노력이 제반되어야 한다. 이를 위해 설치된 폐쇄회로 CCTV와 상황실은 넓은 구역내의 모든 위험상황을 감지하고 대응하기에 미흡하다. 따라서 이러한 영상감시의 미흡한 점을 보완하여 기설치된 CCTV를 통해 위험구역내 보행자를 자동으로 인지하여 큰 사고를 미연에 방지하고자 역사내 지능형 영상감시 시스템을 설계하였다.