• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1017-1021
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• 2013

The traffic signal has been widely used in the transport system with a fixed time interval currently. This kind of setting time was determined based on experience for vehicles to generate a waiting time while allowing pedestrians crossing the street. However, this strict setting causes inefficient problems in terms of economic and safety crossing. In this research, we propose a monitoring algorithm to detect, track and check pedestrian crossing the crosswalk by the patterns of behavior. This monitoring system ensures the safety for pedestrian and keeps the traffic flow in efficient. In this algorithm, pedestrians are detected by using HOG feature which is robust to illumination changes in outdoor environment. According to a complex computation, the parallel process with the GPU as well as CPU is adopted for real-time processing. Therefore, pedestrians are tracked by the relationship of hue channel in image sequence according to the predefined pedestrian zone. Finally, the system checks the pedestrians' crossing on the crosswalk by its HOG based behavior patterns. In experiments, the parallel processing by both GPU and CPU was performed so that the result reaches 16 FPS (Frame Per Second). The accuracy of detection and tracking was 93.7% and 91.2%, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.197-205
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• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• 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.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.2
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• pp.121-128
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• 2013

There have been various research efforts for pedestrian recognition in embedded imaging systems. However, many suffer from their heavy computational complexities. SVM classification method has been widely used for pedestrian recognition. The reduction of candidate region is crucial for low-complexity scheme. In this paper, We propose a real time HOG based pedestrian detection system on GPU which images are captured by a pair of cameras. To speed up humans on road detection, the proposed method reduces a number of detection windows with disparity-search and near-search algorithm and uses the GPU and the NVIDIA CUDA framework. This method can be achieved speedups of 20% or more compared to the recent GPU implementations. The effectiveness of our algorithm is demonstrated in terms of the processing time and the detection performance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.327-327
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• 2010

자동차에서 실내음질은 구매결정 요소들 중의 하나로 그 중요성이 점차 증가하고 있다. 따라서 다양한 운전조건에서 운전자가 기대하는 실내음질의 기대 수준을 충족시켜야 한다. 소비자는 운전경험과 습관에 따라 기대하는 음질에 차이가 있고 소리에 대한 표현방식도 모호하기 때문에 이러한 주관적 특성을 하나의 통일된 표현으로 정의 하기가 어렵다. 그러나 지난 이십여 년 동안의 음질개발과 차량 실내소음 주관평가의 통계처리로 통일된 표현을 할 수 있었다. 나아가 심리음향학 및 신호처리기술의 발달과 꾸준한 음질연구결과로 소리특성을 객관적으로 나타내는 소리의 시각화가 가능하였으며, 운전자가 인식하는 주관평가와의 상관관계를 높여 차량의 대표적인 음질인자로 정량화하여 음질목표를 설정할 수 있었다. 실내소음의 구성은 엔진 투과음, 흡배기 소음, 바람 소음, 도로 기인 소음 등으로 다양하므로 소음원에 따라 음의 균형을 맞추어 조화로운 음질개발을 하는 것이 중요했다. 또한 차량 판매되는 지역에 따라 선호음이 상이하여 지역별 실내음질의 차별화가 필요했다. 궁극적으로는 운전자의 감성품질을 만족할 수 있도록 음을 제어하여 브랜드 사운드를 개발하고 있다. 이러한 실내음질을 달성하기 위한 방법으로 소음원과 전달경로에 대해 기여도를 분석하고, 경로를 구성하는 시스템 별로 세분화하여 시스템 목표를 설정하였다. 시스템 개발에 중요한 인자로 차량의 동강성 및 흡차음 성능을 들 수 있다. 특히 디젤차량의 비중이 큰 유럽업체의 차량의 동강성 및 흡차음 개발 능력은 높게 평가되고 있다. 이에 유럽의 부품전문회사가 가지고 있는 해석과 시험적인 개발 방법을 통하여 전달계 특성을 만족하기 위한 시스템의 동강성 및 흡차음 특성을 개발하고 있다. 차량음질 튜닝의 중요한 기법 중 하나로 흡배기 개발을 추진하고 있다. 친환경자동차인 하이브리드차량, 전기차량 및 연료전지차량의 경우 전기구동부품에서 발생하는 각종 이음 발생을 최소화 했다. 보행자를 보호하고 운전의 즐거움을 향상하기 위한 가상사운드 개발을 진행하고 있다. 회사 수익성 향상을 위한 원가절감 및 구조 경량화에 따른 음질악화와 연비 향상 및 배기가스 규제 강화로 고성능 고출력 엔진탑재에 따른 음질악화 요인을 극복해야 했다. 운전자의 청감은 차량의 운전성에 따라서도 크게 영향을 받게 되므로 엔진제어와 변속기제어를 통해 음질과 운전성이 조화를 이룰 수 있도록 개발하고 있다. 향후, 소음원에 따른 시스템 최적화 개발, 운전성과 음질 연계 개발과 친환경차량의 가상사운드 개발 등이 자동차 음질 개발의 중요한 이슈로 생각한다.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.25-38
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• 2021

The aim of this study is to protect workers and pedestrians from accidents at night or bad weather by attaching optical fiber to existing safety clothing that is made only with fluorescent fabrics and retroreflective materials. A safety vest was designed and manufactured by applying optical fiber, and energy-harvesting technology was developed. The safety vest was designed to emit light using the automatic flashing of optical fibers attached to the film, and an energy harvester was manufactured and attached to drive the light emission of the optical fiber more continuously. As a result, first, the vest wearer' body was recognized from a distance through the optical fiber and retroreflection, which helped prevent accidents. Thus, this concept helps in saving lives by preventing accidents during night-time work on the roadside or activities of rescue crew and sports activities, or by quickly finding the point of an accident with a signal that changes the optical fiber light emission. Second, to use the wasted energy, a piezoelectric-element power generation system was developed and the piezoelectric-harvesting device was mounted. Potentially, energy was efficiently produced by activating the effective charging amount of the battery part and charging it auxiliary. In the existing safety vest, detecting the person wearing the vest is almost impossible in the absence of ambient light. However, in this study, the wearer could be found within 100 m by the light emission from the safety vest even with no ambient light. Therefore, in this study, we will help in preventing and reducing accidents by developing smart safety clothing using optical fiber and energy harvester attached to save lives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.683-686
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• 2018

Autonomous vehicles are the 4th industrial revolution that utilizes artificial intelligence(AI) and superconducting technology, and is a world-wide investment and research project. However, a Uber vehicle under test in Arizona, USA, was accidentally killed by pedestrians crossing the road in the dark night, and accidents occurred when the Tesla vehicle was exposedto the backlightof the sun. These problems were caused by misunderstandings and choice about sensors mounted on autonomous vehicles due to bad weather such as snow, rain, and sunlight. In this paper, we analyze the composition of the autonomous vehicle and the cause of the accident, and consider the criteria that should be judged in case of emergency in which human accidents may occur. This paper analyzes the composition of autonomous vehicles and causes of accidents, and considers the criteria that should be choice in an emergency where an accident may occur.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.264-272
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• 2016

In previous research, we analyzed traffic accident characteristics in the Chungcheong region through factor analysis, cluster analysis, and a questionnaire using traffic accident analysis system data to enhance Korea's traffic safety. Based on the analysis results, we investigated the design and application of traffic safety technology in non-urban areas in this study. Three technologies are proposed to improve traffic safety facilities for the region: a recognition light at pedestrian crossing works, a recognition light on the road for the underprivileged in traffic works, and a safety LED sign for operation of agricultural machine works. Each technology complements the light pollution problem about snow removal and road safety when applied to existing facilities in the non-urban areas. Solar-based indigenous technology is expected to contribute to road safety in rural areas.