• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.62-68
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• 2012

In this paper, a detection method of a pedestrian safety road marking was proposed. The proposed algorithm uses laser range and reflectivity of a range finder (LRF). For a detection of crosswalk marking and stop line, the DFT (Discrete Fourier Transform) of reflectivity and cross-correlation method between the reference replica and the measured reflectivity are used. A speed bump is detected through measuring an altitude difference of two LRFs which have the different tilted angle. Furthermore, we proposed a velocity constrained a detection method of a speed bump. Finally, the proposed methods are tested in on-line, on the pavement of a road. The considered road markings are wholly detected. The localization errors of both road markings are smaller than 0.4 meter.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.3 s.21
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• pp.41-51
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• 2002

Recently, many studies extracting the geography information using digital aerial image have been implemented. But it is very difficult that automatically recognizing objects using edge detection method on the aerial image, and so that work have practiced manually or semi-automatically. Therefore, in this study, we have removed impedimental elements for recognition using the image which overlapped the significant information bands of brightness-sliced aerial images, then have developed the algorithm which can automatically recognize and extract road information and we will try to apply that method when we develope a system. For this, first of all, we have developed the 'template conformal-transformation moving operator' for automatically recognizing crosswalk area from crosswalk band image and the 'window normal search algorithm' which is able to track road area based on long-side length of crosswalk, so that we have proposed the method that can extract directly the road information from the aerial image.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.631-637
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• 2010

In this paper, We propose a method of motion interpretation of pedestrian for active traffic signal control. We detect pedestrian object in a movie of crosswalk area by using the code book method and acquire contour information. To do this stage fast, we use parallel processing based on CUDA (Compute Unified Device Architecture). And we remove shadow which causes shape distortion of objects. Shadow removed object is judged by using the hilbert scan distance whether to human or noise. If the objects are judged as a human, we analyze pedestrian objects' motion, face area feature, waiting time to decide that they have intetion to across a crosswalk for pdestrians. Traffic signal can be controlled after judgement.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.127-136
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• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.320-328
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• 2021

As the number of visually impaired people steadily increases, interest in independent walking is also increasing. However, there are various inconveniences in the independent walking of the visually impaired at present, reducing the quality of life of the visually impaired. The white cane, which is an existing walking aid for the visually impaired, has difficulty in recognizing upper obstacles and obstacles outside the effective distance. In addition, it is inconvenient to cross the street because the sound signal to help the visually impaired cross the crosswalk is lacking or damaged. These factors make it difficult for the visually impaired to walk independently. Therefore, we propose the design of an embedded system that provides traffic light recognition through object recognition technology, voice guidance using TTS, and upper obstacle recognition through ultrasonic sensors so that blind people can realize safe and high-quality independent walking.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1184-1186
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• 2022

횡단보도에서의 보행자 교통사고 방지를 위한 다양한 방법들이 연구되고 있다. 본 논문에서는 점멸 신호등 상황에서 보행자 교통사고를 감소시키기 위해 영상을 이용한 심층 신경망 기반 횡단보도 보행자 검출 방법을 소개한다. YOLOv5 와 Faster R-CNN 각각을 기반으로 다양한 버전의 횡단보도 보행자 검출기를 구현하고, 이번 실험에서 중점이 되는 이들의 수행 시간을 비교 평가하고 mAP@0.5 가 어느 정도인지 판단하여 가장 적합한 모델을 판단한다. 실험 결과 실시간 처리 측면에서 YOLOs 모델이 84 fps 를 달성함으로써 실시간 보행자 검출에 가장 좋은 성능을 보였다. 횡단보도의 상황은 상시 빠르게 변하므로 가장 빠른 처리 성능을 기록한 YOLOv5s 모델이 실시간 횡단보도 보행자 검출 시스템에 가장 적합한 것으로 판단된다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.51-59
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• 2024

In this paper, we propose a novel traffic safety system designed to reduce pedestrian traffic accidents and enhance safety on university campuses. The system involves real-time detection of vehicle speeds in designated areas and the interaction between vehicles and pedestrians at crosswalks. Utilizing the YOLOv5s model and Deep SORT method, the system performs speed measurement and object tracking within specified zones. Second, a condition-based output system is developed for crosswalk areas using the YOLOv5s object detection model to differentiate between pedestrians and vehicles. The functionality of the system was validated in real-time operation. Our system is cost-effective, allowing installation using ordinary smartphones or surveillance cameras. It is anticipated that the system, applicable not only on university campuses but also in similar problem areas, will serve as a solution to enhance safety for both vehicles and pedestrians.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1051-1058
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• 2008

The advancement of technology for image processing and communications makes it possible for current traffic signal controllers and vehicle detection technology to make both emergency vehicle preemption and transit priority strategies as a part of integrated system. Present]y traffic signal control in crosswalk is controlled by fixed signals. The signal control keeps regular signals traffic even with no traffic, when there is traffic, should wait until the signal is given. Waiting time causes the risk of traffic accidents and traffic congestion in accordance with signal violation. To help reduce the risk of accidents and congestion, this paper explains traffic signal control system for the adaptive priority order so that signal may be preferentially given in accordance with the situation of site through the object detect images.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.19-25
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• 2013

Recently, more accurate database information of facilities is being required, with the increase in importance of urban road facility management. Therefore, this study proposed how to automatically detect particular traffic signs necessary for efficient construction of road facility DB. For this study, central locations of facilities were searched, after recognition and automatic detection of particular traffic signs through an image. Then, coordinate values of traffic signs calculated in the study were compared with real coordinate values, in order to evaluate the accuracy of traffic sign locations which were finally detected. Computer vision technology was used in recognizing and detecting traffic signs through OPEN CV-based coding, and photogrammetry was used in calculating accurate locations of detected traffic signs. For the experiment, circular road signal(No Parking) and triangular road signal(Crosswalk) were chosen out of various kinds of road signals. The research result showed that the circular road signal had a nearly 50cm error value, and the triangular road signal had a nearly 60cm error value, when comparing the calculated coordinates with the real coordinates. Though this result is not satisfactory, it is considered that there would be no problem to find locations of traffic signs.