• Journal of KIISE
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• v.42 no.2
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• pp.203-212
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• 2015

This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.36-54
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• 2017

The purpose of this paper is to develop a multi-sensor fusion-based traffic information acquisition system with robust to environmental changes. it combines the characteristics of each sensor and is more robust to the environmental changes than the video detector. Moreover, it is not affected by the time of day and night, and has less maintenance cost than the inductive-loop traffic detector. This is accomplished by synthesizing object tracking informations based on a radar, vehicle classification informations based on a video detector and reliable object detections of a infrared range finder. To prove the effectiveness of the proposed system, I conducted experiments for 6 hours over 5 days of the daytime and early evening on the pedestrian - accessible road. According to the experimental results, it has 88.7% classification accuracy and 95.5% vehicle detection rate. If the parameters of this system is optimized to adapt to the experimental environment changes, it is expected that it will contribute to the advancement of ITS.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.45-50
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• 2009

This paper presents an active vision system for on-line traffic sign recognition. The system is composed of two cameras, one is equipped with a wide-angle lens and the other with a telephoto lends, and a PC with an image processing board. The system first detects candidates for traffic signs in the wide-angle image using color, intensity, and shape information. For each candidate, the telephoto-camera is directed to its predicted position to capture the candidate in a large size in the image. The recognition algorithm is designed by intensively using built in functions of an off-the-shelf image processing board to realize both easy implementation and fast recognition. The results of on-road experiments show the feasibility of the system.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.372-375
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• 2008

IEEE 802.11e standardized the EDCA mechanism to support the priority based QoS. And the virtual collision handler schedules the transmission time of each MAC frame using the internal back-off window according to the access category(AC). This can provides the differentiated QoS to real-time services at the medium traffic load condition. However, the transmission delay of MAC frame for real-time services may be increased as the traffic load of best effort service increases. It becomes more critical when the real-time service uses a compressed mode video codec such as moving picture experts group(MPEG) 4 codec. That is because each frame has the different importance. That is, the I-frame has more information as compared with the P- and the B-frame. In this paper, we proposed a buffer management algorithm based on the frame importance and the delay bound. The proposed algorithm is consisted of the traffic regulator based on the dual token bucket algorithm and the active queue management algorithm. The traffic regulator reduces the transmission rate of lower AC until that the virtual collision handler can transmit an I-frame. And the active queue management discards frame based on the importance of each frame and the delay bound of head of line(HoL) frame when the channel resource is insufficient.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.121-129
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• 2023

In this paper, we developed a traffic signal control system for emergency situations that can minimize loss of property and life by actively controlling traffic signals in a certain section in response to emergency situations. When the emergency vehicle terminal transmits an emergency signal including identification information and GPS information, the surrounding image is obtained from the camera, and the object is analyzed based on deep learning to output object information having information such as the location, type, and size of the object. After generating information tracking this object and detecting the signal system, the signal system is switched to emergency mode to identify and track the emergency vehicle based on the received GPS information, and to transmit emergency control signals based on the emergency vehicle's traveling route. It is a system that can be transmitted to a signal controller. This system prevents the emergency vehicle from being blocked by an emergency control signal that is applied first according to an emergency signal, thereby minimizing loss of life and property due to traffic obstacles.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.63-71
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• 2006

There were many theories to suggest the indicators that evaluate stability and reflect the plan in mixed traffic street in all traffic modes This Paper analyzed residential street of Suwon City adopting time-space occupancy index among these indicates. Three survey street. congregated into apartment house. were selected to analyze this and pictured by video camera over one hour. and the length of these street were 76m, 55m, and 34m each, and major street by each region, comparably high in Pedestrians and pass vehicles, were selected. Basic datum to calculate time-space occupancy index and time-space occupancy index per person was gathered through video analysis. i.e Parameters for pedestrians, cars, bicycles, average speed of bicycles, and parking car. There are some limitation to analyze LOS of mixed traffic street in all traffic modes, not pedestrian mall. Therefore this paper presented evaluation standard of LOS. In conclusion. the aim of this paper is to suggest modeling based on guidelines for evaluating LOS of mixed traffic street in all traffic modes. It is estimated that this will be influencing indicates for improving pedestrian environment, and Planning mixed traffic street in future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.97-105
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• 2020

Recently, for a smart city, there is a demand for a technology for acquiring traffic information using an intelligent road infrastructure and managing it. In the meantime, various technologies such as loop detectors, ultrasonic detectors, and image detectors have been used to analyze road traffic information but these have difficulty in collecting various informations, such as traffic density and length of a queue required for building a traffic information DB for moving vehicles. Therefore, in this paper, assuming a smart city built on the basis of a camera infrastructure such as intelligent CCTV on the road, a solution for calculating the traffic DB of moving vehicles using Vision Tracking of road CCTV cameras is presented. Simulation and verification of basic performance were conducted and solution can be usefully utilized in related fields as a new intelligent traffic DB calculation solution that reflects the environment of road-mounted CCTV cameras and moving vehicles in a variable smart city road environment. It is expected to be there.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.564-566
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• 2015

Red traffic light indicates a different meaning, the green one. Motorists and pedestrians in the crosswalk must stop or move in accordance with a signal light. However, the probability of an accident is high if you do not see or ignore these signals. In addition to the curved pedestrian crossing installed the announcement system using an infrared sensor is difficult. In this paper, we design and implement methods of detecting pedestrians using the camera. A camera installed on the pontoon walking up the pedestrian and the pedestrian detection range is set through the image. With the proposed system it is effective in detecting pedestrians in the crosswalk curved.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.123-128
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• 2017

This paper deals with performing missions through autonomous navigation using camera and other sensors. Extracting pose of the car is necessary to navigate safely within the given road. Homography is used to find it. Color image is converted into grey image and thresholding and edge is used to find control points. Two control ponits are converted into world coordinates using homography to find the angle and position of the car. Color is used to find traffic signal. It was confirmed that the given tasks performed well through experiments.

• Journal of information and communication convergence engineering
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• v.4 no.3
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• pp.123-129
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• 2006

The limited number of roads and the increasing number of vehicles demand the automatic regulation of overspeed vehicles, illegal vehicles, and overloaded vehicles and the automatic charge calculation depending on the type of the vehicle. To meet such requirements, it is important to remove the shadow of the vehicle as processing and recognizing an image captured by a camera. The shadow of the vehicle is likely to cause misclassification of the vehicle type due to diverse errors and mistakes occurring when detecting geometrical properties of the vehicle. In case that shadows of two different vehicles are overlapped, not only the type of the vehicles may be misclassified but also it is difficult to accurately identify the type of the vehicles. In this paper, we propose a robust algorithm to remove the shadow of a vehicle by calculating the luminance, the chrominance, the gradient density of the cast shadow from information acquired using the image subtraction of the background, and to recognize the substantial vehicle figure. Even when it is hard to detect and split a target vehicle from its shadow as shadows of vehicles are attached to each other, our robust algorithm can detect the vehicle figure only. We implemented our system with a general camera and conducted experiments on various vehicles on general roads to find out our vehicle shade removal algorithm is efficient when detecting and recognizing vehicles.