• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.19-27
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• 2019

This paper proposes a new method using multi-layer overlapped windows to improve the performance of YOLO network which is vulnerable to detect small dense objects. In particular, the proposed method uses the YOLO Network based on the multi-layer overlapped windows to track small dense vehicles that approach from long distances. The method improves the detection performance for location and size of small vehicles. It allows crossing area of two multi-layer overlapped windows to track moving vehicles from a long distance to a short distance. And the YOLO network is optimized so that GPU computation time due to multi-layer overlapped windows should be reduced. The superiority of the proposed algorithm has been proved through various experiments using captured images from road surveillance cameras.

• Journal of Platform Technology
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• v.9 no.4
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• pp.60-70
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• 2021

This paper proposes a method capable of stably tracking the coordinates of a patterned laser image in a curved screen shooting system using two or more channels of multiple cameras. This method can track and acquire target points very effectively when applied to a multi-screen shooting method that can replace the HMD shooting method. Images of curved screens with severe deformation obtained from individual cameras are corrected through image normalization, image binarization, and noise removal. This corrected image is created and applied as an Euclidean space map that is easy to track the firing point based on the matching point. As a result of the experiment, the image coordinates of the pattern laser were stably extracted in the curved screen shooting system, and the error of the target point position of the real-world coordinate position and the broadband Euclidean map was minimized. The reliability of the proposed method was confirmed through the experiment.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.175-182
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• 2017

In this paper, we present a multi-depth generation method using a time-of-flight (ToF) fusion camera system. Multi-view color cameras in the parallel type and ToF depth sensors are used for 3D scene capturing. Although each ToF depth sensor can measure the depth information of the scene in real-time, it has several problems to overcome. Therefore, after we capture low-resolution depth images by ToF depth sensors, we perform a post-processing to solve the problems. Then, the depth information of the depth sensor is warped to color image positions and used as initial disparity values. In addition, the warped depth data is used to generate a depth-discontinuity map for efficient stereo matching. By applying the stereo matching using belief propagation with the depth-discontinuity map and the initial disparity information, we have obtained more accurate and stable multi-view disparity maps in reduced time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.553-560
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• 2010

This study examined the factors influencing the number of enforcement cases on traffic signal violation. The study collected enforcement data on traffic signal violation by multi-functional unmanned cameras across the Daegu metropolitan area, and analyzed the data to determine correlations among various factors including physical dimensions and engineering characteristics of the roadway as to how they would influence the number of violations. The resulting analysis provided a general model that assesses applicability for multi-functional unmanned cameras, and further shed helpful insights on efficient utilization of such traffic signal enforcement equipments in terms of installation thresholds and location. By identifying violation-prone features and characteristics and subsequent enforcement considerations, this research also supports safety efforts in reducing the number of traffic accidents as well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.76-79
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• 2010

Internet of the Web-based Home Securiy, ITS (Intelligent Traffic System), the tourism industry, production field, etc In many fields, using a network camera imaging system has been spotlighted and Accordingly the demand for network cameras is growing rapidly. in order to control it according to the video server complex has a costly problem. In this paper, according to an increasing number of cameras cost and complexity of the video server problems to solve information from video cameras through multi-channel input single-channel multiplex and the fact that switching is handled and Also, the system automatically switches the image data is implemented.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.35-41
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• 2015

When a video surveillance system tracks a specific object, it is very important to get quickly the information of the object through fast image processing. Usually one camera surveillance system for tracking the object made results in various problems such like occlusion, image noise during the tracking process. It makes difficulties on image based moving object tracking. Therefore, to overcome the difficulties the multi video surveillance system which installed several camera within interested area and looking the same object from multi angles of view could be considered as a solution. If multi cameras are used for tracking object, it is capable of making a decision having high accuracy in more wide space. This paper proposes a method of recognizing and tracking a specific object like a car using the homography in which multi cameras are installed at the crossroad.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.539-548
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• 2013

Although Motion estimation (ME) plays an important role in digital video compression, it requires a complicated search procedure to find an optimal motion vector. Multi-view video is obtained by capturing one three-dimensional scene with many cameras at different positions. The computational complexity of motion estimation for Multi-view video coding increases in proportion to the number of cameras. To reduce computational complexity and maintain the image quality, a low complexity motion estimation search method is proposed in this paper. The proposed search method consists of four-grid diamond search patten, two-gird diamond search pattern and TZ 2 Point search pattern. These search patterns exploit the characteristics of the distribution of motion vectors to place the search points. Experiment results show that the speedup improvement of the proposed method over TZ search method (JMVC) can be up to 1.8~4.5 times faster by reducing the computational complexity and the image quality degradation is about to 0.01~0.24 (dB).

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.149-154
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• 2023

This paper presents a novel approach to multi-focus image fusion using light field cameras. The proposed neural network, LFFCNN (Light Field Focus Convolutional Neural Network), is composed of three main modules: feature extraction, feature fusion, and feature reconstruction. Specifically, the feature extraction module incorporates SPP (Spatial Pyramid Pooling) to effectively handle images of various scales. Experimental results demonstrate that the proposed model not only effectively fuses a single All-in-Focus image from images with multi focus images but also offers more efficient and robust focus fusion compared to existing methods.

• Journal of Broadcast Engineering
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• v.12 no.4
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• pp.333-341
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• 2007

Multi-view video has recently gained much attraction from academic and commercial fields because it can deliver the immersive viewing of natural scenes. This paper presents multi-view video processing being composed of intermediate view reconstruction (IVR), graphics composition, and multi-view video viewer. First we generate virtual views between multi-view cameras using depth and texture images of the input videos. Then we mix graphic objects to the generated view images. The multi-view video viewer is developed to examine the reconstructed images and composite images. As well, it can provide users with some special effects of multi-view video. We present experimental results that validate our proposed method and show that graphic objects could become the inalienable part of the multi-view video.

• 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.