• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.452-460
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• 1994

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading To automatically extract a road map directly from more complicated topographical maps, a very complicated algorithm is needed, since the image generally involves such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.107-116
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• 2014

This paper presents camera-based road surface marking detection methods suited to sensor fusion-based positioning system that consists of low-cost GPS (Global Positioning System), INS (Inertial Navigation System), EDM (Extended Digital Map), and vision system. The proposed vision system consists of two parts: lane marking detection and RSM (Road Surface Marking) detection. The lane marking detection provides ROIs (Region of Interest) that are highly likely to contain RSM. The RSM detection generates candidates in the regions and classifies their types. The proposed system focuses on detecting RSM without false detections and performing real time operation. In order to ensure real time operation, the gating varies for lane marking detection and changes detection methods according to the FSM (Finite State Machine) about the driving situation. Also, a single template matching is used to extract features for both lane marking detection and RSM detection, and it is efficiently implemented by horizontal integral image. Further, multiple step verification is performed to minimize false detections.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.259-266
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• 2000

Crash test data from different impact modes and threshold speeds were used to assess the effects of impact conditions on air bag electronic single point sensing (ESPS) activation requirements. The requirements are expressed in terms of the desired sensor activation time based on unbelted driver dummy kinematics. A crash discriminator pre-displacement is introduced to crash recognition algorithm to the ESPS. The new crash recognition algorithm named Velocity Energy Pre-displacement(VEPD) method is developed and the ESPS algorithm based on the VEPD technique is used to assess the ESPS system performance. It is shown that VEPD method correlates very well with desired sensor activation time and meets the activation requirement.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1211-1212
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• 2008

In this paper, we propose image process modeling as a part of location based surveillance system for unauthorized target recognition and tracking in harbor, airport, military zone. For this, we compress and store background image in lower resolution and perform object extraction and motion tracking by using sobel edge detection and difference picture method between real images and a background image. In addition to, we use Independent Component Analysis Neural Network for moving target recognition. Experiments are performed for object extraction and tracking of moving targets on road by using static camera in 20m height building and it shows the robust results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.10-21
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• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.293-296
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• 2016

In case of an emergency on a busy road of a city, drivers should make way for special vehicles such as police cars, fire engines, or ambulance as soon as possible. If road infrastructures recognize the movements of special vehicles, and transfer alert message to traffic signal controllers and normal cars through wireless network such as WAVE or TPEG, normal cars can prepare to make way in advance. As a result, it help special vehicles move faster. In this paper, we install a roof mark on the roof of a special vehicle, detect the mark through a mark recognition algorithm which includes perspective transformation, and get the inner information by decoding the digital pattern on it. The experiment results show that mark can be recognized 100% and 93.3% of inner digital data of the mark can be recognized, when the size of a mark is larger than $88cm{\times}88cm$ and the mark moves at a speed of 50km/s.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1723-1728
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• 2021

C-ITS is an intelligent transportation system that can improve transportation convenience and traffic safety by collecting, managing, and providing traffic information between components such as vehicles, road infrastructure, drivers, and pedestrians. In Korea, road infrastructure is being built across the country through the C-ITS project, and various services such as real-time traffic information provision and bus operation management are provided. However, the current state-of-the-art road infrastructure and information linkage system are insufficient to build C-ITS. In this paper, considering the continuity of time in various spatial aspects, we proposed a group-type network-based traffic situation recognition system that can recognize traffic flows and unexpected accidents through information linkage between traffic infrastructures. It is expected that the proposed system can primarily respond to accident detection and warning in the field, and can be utilized as more diverse traffic information services through information linkage with other systems.

• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.21-32
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• 2006

The purpose of this study is to determine the economical standard for road signs by verifying the difference in driver's legibility time with respect to the spacing of letters on the toad signs. Laboratory simulations were conducted to confirm the difference in legibility time for six target signs of different spacing. Also. a binary logit model was used to find the main factors, which could lower the rate if misreading. This model involves not only a simple enlargement of signs but also a suitable match of letters and signs along with the optimal spacing of the text letters on the road signs to increase the legibility of the sign. The result of this study verified the importance of spacing in road signs and Proved itself to be an effective method to determine the future standard for the road signs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1437-1444
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• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.