• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.364-366
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• 2004

WDR(Wide Dynamic Range) camera has been recently introduced to provide good detailed information for the extremely dark or white area. The double shuttering camera acquires two pictures with different exposure time for the same scenes so that each image has its unique information as for the bright/dark area. Those images are combined internally to produce an image with enough details. This paper proposes a NN based method to control the exposure time of the WDR camera. Our goal is to develop a method to automatically control the exposure time like human decision. A neural model is trained to determine to increase/decrease shutter time for the given situation. The ability to adapt to unknown situation is shown for the sample cases.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1103-1109
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• 2014

The latest CCTV camera are network camera in many cases. In this case when transmitting high-quality image by internet, it could be a large load on the internet because the amount of image data is very large. In this study, we propose a method which can reduce the video traffic in this case, and evaluate its performance. We used a method for transmitting and extracting a gesture information using ToF camera such as Kinect in certain circumstances. There may be restrictions on the application of the proposed method because it depends on the performance of the ToF camera. However, it can be applied efficiently to the security or safety management of a small interior space such as a home or office.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.107-118
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• 2006

Recently, network infra grows rapidly and the digital image compression technique have made remarkable progress, and therefore the demand of the real-time image surveillance system which uses a network camera server is increasing. Network Camera Server has emerged as an attractive alternative to the CCTV for the real-time image surveillance. From this reason, the demand regarding the development of the real-time image surveillance system which uses a network camera server is increasing as well. In this paper, we will provide a model for JPEG Streamer which is used in real-time image surveillance system. And we will design and implement this model. For the stability and efficiency of the JPEG Streamer, we'll use the thread pool and shared memory. We will also introduce the concept of double buffering for increasing the quality of real-time image, Using the JPEG Streamer, a raising of the productivity, reliability and stability will be able to secure to development of real-time image surveillance system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11A
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• pp.1066-1072
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• 2010

In this paper we proposed a multi event handling surveillance system using multiple PTZ cameras. One event is assigned to each PTZ camera to detect unusual situation. If a new object appears in the scene while a camera is tracking the old one, it can not handle two objects simultaneously. In the second case that the object moves out of the scene during the tracking, the camera loses the object. In the proposed method, the nearby camera takes the role to trace the new one or detect the lost one in each case. The nearby camera can get the new object location information from old camera and set the seamless event link for the object. Our simulation result shows the continuous camera-to-camera object tracking performance.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.733-740
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• 2001

A monitoring robot capable of doing network and RF communication is introduced. The robot has several features that poses arbitrary position thanks to a mechanism combining the 4wheel drive and 4 link mechanism, transmits an image and command data via RF wireless communication. Moreover, the image data from the camera are transferred through a network communication. The robot plays a role in monitoring what is happening around the robot, and covers wide range due to a moving camera associated with the 4 arms. The robot can adjust its mass center by the 4 link mechanism, hence it guarantees a stability in moving on the slope.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.151-156
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• 2018

In this paper, a method to classify objects in outdoor CCTV images using Convolutional Neural Network(CNN) and background subtraction is proposed. Object candidates are extracted using background subtraction and they are classified with CNN to detect objects in the image. At the end, computation complexity is highly reduced in comparison to other object detection algorithms. A database is constructed by filming alleys and playgrounds, places where crime occurs mainly. In experiments, different image sizes and experimental settings are tested to construct a best classifier detecting person. And the final classification accuracy became 80% for same camera data and 67.5% for a different camera.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.16-21
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• 2003

In the agricultural field, a machine vision system has been widely used to automate most inspection processes especially in quality grading. Though machine vision system was very effective in quantifying geometrical quality factors, it had a deficiency in quantifying color information. This study was conducted to evaluate color of beef using machine vision system. Though measuring color of a beef using machine vision system had an advantage of covering whole lean tissue area at a time compared to a colorimeter, it revealed the problem of sensitivity depending on the system components such as types of camera, lighting conditions, and so on. The effect of color balancing control of a camera was investigated and multi-layer BP neural network based color calibration process was developed. Color calibration network model was trained using reference color patches and showed the high correlation with L*a*b* coordinates of a colorimeter. The proposed calibration process showed the successful adaptability to various measurement environments such as different types of cameras and light sources. Compared results with the proposed calibration process and MLR based calibration were also presented. Color calibration network was also successfully applied to measure the color of the beef. However, it was suggested that reflectance properties of reference materials for calibration and test materials should be considered to achieve more accurate color measurement.