• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.188-194
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• 2008

We propose the new method for the traffic signs recognition that is one of the DAS(Driving assistance system) in the intelligent vehicle. Our approach estimates a varied degree by using a geometric method from the varied traffic signs in noise, rotation and size, and extracts the recognition symbol from the compensated traffic sign for a recognition by using the sequential color-based clustering. This proposed clustering method classify the traffic sign into the attention, regulation, indication, and auxiliary class. Also, The circular tracing method is used for the final traffic sign recognition. To evaluate the effectiveness of the proposed method, varied traffic signs were built. As a result, The proposed method show that the 95 % recognition rate for a single variation, and 93 % recognition rate for a mixed variation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.406-406
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• 2000

In this study, as a part developing an unmanned container terminal (UCT), Ive designed and implemented an Autonomous Ground Vehicle (AGV) that can deliver containers in the port fast and safely as they are scheduled. It is preferable to research the intelligent UCT/AGV for delivering containers all day long without causing any trouble. For the sake of safe and fast AGV driving, we implemented a multiple-sensor system with vision, ultrasonic, and IR sensors and we adapted the hight-speed wireless LAN that satisfies the IEEE 802.11 Standard for hi-directional communication between the main processor in AGV and a host computer. The Pentium-III processor board mounted on the bottom frame in AGV combines and computes the information from sensors and controls the AGV driving. There are also the 80C196KC micro-controllers to control the actuating and steering motors. In addition, a steering function that is defined newly in this paper is heavily concerned in the mechanical design, and it plays an important role when AGV moves along a curve. Experimental results show the fast and safe delivery operations are possible with this UCT/AGV

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.498-506
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• 2003

A computer vision system applied to an intelligent safety vehicle has been required to be worked on a small sized real time special purposed hardware not on a general purposed computer. In addition, the system should have a high reliability even under the adverse road traffic environment. This paper presents a design and an implementation of an onboard hardware system taking into account for high speed image processing to analyze a road traffic scene. The system is mainly composed of two parts: an early processing module of FPGA and a postprocessing module of DSP. The early processing module is designed to extract several image primitives such as the intensity of a gray level image and edge attributes in a real-time Especially, the module is optimized for the Sobel edge operation. The postprocessing module of DSP utilizes the image features from the early processing module for making image understanding or image analysis of a road traffic scene. The performance of the proposed system is evaluated by an experiment of a lane-related information extraction. The experiment shows the successful results of image processing speed of twenty-five frames of 320$\times$240 pixels per second.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.107-112
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• 2018

In this paper, optical flow based keypoint detection and tracking technique is proposed for the collaboration between flying drone with vision system and ground robots. There are many challenging problems in target detection research using moving vision system, so we combined the improved FAST algorithm and Lucas-Kanade method for adopting the better techniques in each feature detection and optical flow motion tracking, which results in 40% higher in processing speed than previous works. Also, proposed image binarization method which is appropriate for the given marker helped to improve the marker detection accuracy. We also studied how to optimize the embedded system which is operating complex computations for intelligent functions in a very limited resources while maintaining the drone's present weight and moving speed. In a future works, we are aiming to develop collaborating smarter robots by using the techniques of learning and recognizing targets even in a complex background.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.398-404
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• 2015

In this paper, we describe the design and performance of a prototype multi-rotor unmaned aerial vehicle( UAV) platform featuring an inertial measurement unit(IMU) based autonomous-flying for use in bluetooth communication environments. Although there has been a fair amount of study of free-flying UAV with multi-rotors, the more recent trend has been to outfit hexarotor helicopter with gimbal to support various services. This paper introduces the hardware and software systems toward very compact and autonomous hexarotors, where they can perform search, rescue, and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices or vision system. The proposed system comprises the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling and simulation in the helicopter. Furthermore, the hexarotor helicopter with implemented IMU is connected with a micro controller unit(MCU)(ARM-cortex) board. The micro-controller is able to command the rotational speed of the rotors and to get the measurements of the IMU as input signals. The control simulation and experiment on the real system are implemented in the test platform, evaluated and compared against each other.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.56-63
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• 2003

This paper introduces a vision based autonomous mini mobile robot that is an approach to produce real autonomous vehicle. Previous autonomous vehicles are dependent on PC, because of complexity of designing hardware, difficulty of installation and abundant calculations. In this paper, we present an autonomous motile robot system that has abilities of accurate steering, quick movement in high speed and intelligent recognition as a stand-alone system using a mono camera. The proposed system has been implemented on mini track of which width is 25~30cm, and length is about 200cm. Test robot can run at average 32.9km/h speed on straight lane and average 22.3km/h speed on curved lane with 30~40m radius. This system provides a model of autonomous mobile robot adapted a lane recognition algorithm in odor to make real autonomous vehicle easily.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.63-70
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• 2003

Real-time information on vehicle queue at intersections is essential for optimal traffic signal control, which is substantial part of Intelligent Transport Systems (ITS). Computer vision is also potentially an important element in the foundation of integrated traffic surveillance and control systems. The objective of this research is to propose a method for detecting an exact queue lengths at signalized intersections using image processing techniques and a neural network model Fuzzy ARTMAP, which is a supervised and self-organizing system and claimed to be more powerful than many expert systems, genetic algorithms. and other neural network models like Backpropagation, is used for recognizing different patterns that come from complicated real scenes of a car park. The experiments have been done with the traffic scene images at intersections and the results show that the method proposed in the paper could be efficient for the noise, shadow, partial occlusion and perspective problems which are inevitable in the real world images.

• Journal of the Semiconductor & Display Technology
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• v.17 no.2
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• pp.76-80
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• 2018

In recent years, with the development of automobile technology, smart system technology that assists safe driving has been developed. A camera is installed on the front and rear of the vehicle as well as on the left and right sides to detect and warn of collision risks and hazards. Beyond the technology of simple black-box recording via cameras, we are developing intelligent systems that combine various computer vision technologies. However, most related studies have been developed to optimize performance in laboratory-like environments that do not take environmental factors such as weather into account. In this paper, we propose a method to detect object by restoring visibility in image with degraded image due to weather factors such as fog. First, the image quality degradation such as fog is detected in a single image, and the image quality is improved by restoring using an intermediate value filter. Then, we used an adaptive feature extraction method that removes unnecessary elements such as noise from the improved image and uses it to recognize objects with only the necessary features. In the proposed method, it is shown that more feature points are extracted than the feature points of the region of interest in the improved image.