• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• Proceedings of the Korea Society of Costume Conference
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• 2003.10a
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• pp.68-68
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• 2003

Recently, street fashion has been regularly introduced in many fashion media because both consumers and marketers began to recognize the importance of street fashion as a meaningful and objective fashion information source. As the globalization proceeds in fashion field,' the street fashion informations in major cities which has led world fashion trends become more influential on domestic fashion, but little concern has been paid on it. This study classified women's street fashion in 4 major world fashion cities such as Paris, London, New York, Tokyo and identified style characteristics of each group. For data collection, 795 front-view photos were selected from the two fashion trade publication 'STREET' and 'VIEW' which has introduced street fashion photos in those cities from 1996. Classification process went on three stages: sorting, naming and grouping. 49 undergraduate students were divided into 12 teams and about 80 photos were given to each team to sort into several sub-groups by overall images or common style characteristics. Then each sub-group was named according to common images or characteristics. Final groups came out after grouping each sub-group with a similar or same title together. For each group, common style characteristics were analyzed.

• ETRI Journal
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• v.39 no.1
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• pp.97-107
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• 2017

A fast and accurate building-level visual place recognition method built on an image-retrieval scheme using street-view images is proposed. Reference images generated from street-view images usually depict multiple buildings and confusing regions, such as roads, sky, and vehicles, which degrades retrieval accuracy and causes matching ambiguity. The proposed practical database refinement method uses informative reference image and keypoint selection. For database refinement, the method uses a spatial layout of the buildings in the reference image, specifically a building-identification mask image, which is obtained from a prebuilt three-dimensional model of the site. A global-positioning-system-aware retrieval structure is incorporated in it. To evaluate the method, we constructed a dataset over an area of $0.26km^2$. It was comprised of 38,700 reference images and corresponding building-identification mask images. The proposed method removed 25% of the database images using informative reference image selection. It achieved 85.6% recall of the top five candidates in 1.25 s of full processing. The method thus achieved high accuracy at a low computational complexity.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• Journal of information and communication convergence engineering
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• v.10 no.4
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• pp.411-419
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• 2012

This paper proposes a semi-automatic image-based approach for 3-dimensional (3D) modeling of buildings along streets. Image-based urban 3D modeling techniques are typically based on the use of aerial and ground-level images. The aerial image of the relevant area is extracted from publically available sources in Google Maps by stitching together different patches of the map. Panoramic images are common for ground-level recording because they have advantages for 3D modeling. A panoramic video recorder is used in the proposed approach for recording sequences of ground-level spherical panoramic images. The proposed approach has two advantages. First, detected camera trajectories are more accurate and stable (compared to methods using multi-view planar images only) due to the use of spherical panoramic images. Second, we extract the texture of a facade of a building from a single panoramic image. Thus, there is no need to deal with color blending problems that typically occur when using overlapping textures.

• KIEAE Journal
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• v.7 no.4
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• pp.31-38
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• 2007

Streetscape plays on important role in urban cities. Characteristics of streets is basically defined by the building facade, signs, plants and paving during the daytime. However at night, street receives a new appearance due to the shop light from the buildings, exterior lightings, signboards and street light, and it creates most of the image of the streets. This study aims to analyze the luminous environment and its subjective image of two different pedestrian's commercial streets. Insa-dong street and Myungdong street were chosen for the study. Horizontal illuminance and luminance on building surface, advertisement billboards and pedestrian road were measured. Thirty students were asked to rate the five scaled questionnaire on their subjective images of the streets. Statistical analysis including profile, correlation and T-test are conducted and some findings are discussed

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.605-613
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• 2018

In this paper, we propose a system to walk around the world with virtual reality technology. Although the virtual reality users are interested in the virtual travel contents, the conventional virtual travel contents have limited space for experiencing and lack of interactivity. In order to solve the problem of lack of realism and limited space, which is a disadvantage of existing contents, this system created a virtual space using Google Street View image. Users can have realistic experience with real street images, and travel a vast area of the world provided by Google Street View image. Also, a virtual reality headset and a treadmill equipment are used so that the user can actually walk in the virtual space, which maxmizes user interactivity and immersion. We expect this system contributes to the leisure activities of virtual reality users by allowing natural walking trip from famous tourist spots to even mountain roads and alleys.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1403-1410
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• 2020

Recently, as interest in non-face-to-face experiences and services increases, the demand for web video contents that can be easily consumed using mobile devices such as smartphones or tablets is rapidly increasing. To cope with these requirements, in this paper we propose a technique to efficiently produce video contents that can provide experience of visiting famous places (i.e., stage tour) in animation or movies. To this end, an image dataset was established by collecting images of stage areas using Google Maps and Google Street View APIs. Afterwards, a deep learning-based style transfer method to apply the unique style of animation videos to the collected street view images and generate the video contents from the style-transferred images was presented. Finally, we showed that the proposed method could produce more interesting stage-tour video contents through various experiments.

• Archives of design research
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• v.18 no.4 s.62
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• pp.287-296
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• 2005

The street image in a business area is so much affected by Facade that the front side of a roadside building makes. Recently, for the indiscreet and intemperate advertising signboard of the front side of roadside buildings, a streetscape becomes more disordered than before, so now we need to do research about signboards of roadside buildings for a streetscape image. In this research, we focused on a streetscape with difference of occupation density of signboard in the business area via investigation and analysis about occupation density of signboards of the front side of roadside buildings, and we suggested optimum occupation density of signboards for supporting the road image positively. An object of research is the street in the business area that has many pedestrians and active passing zone of cars. We investigated and analyzed how to feel street images on the rate of occupation density of roadside building's signboards of in the chosen street. As a result of using an adjective that we use for estimating street view images for extraction of street images, we could know 2 factors. We named that one is the image of recognition, and the other is the image of feelings. We knew that signboard density of street of heavily recognized images is from 20% to 30% and, signboard density of street of heavily feeling images is from 50% to 60%. We also could know that people feel both images of recognition and images of feeling in specific density, 30 to 50%. Through this result of research, we can suggest Facade on signboard density with the recognition and the feeling and use images of the street view as materials to be more specific and more special.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.515-518
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• 2019

Semantic segmentation has remained as a challenging problem in the field of computer vision. Given the immense power of Convolution Neural Network (CNN) models, many complex problems have been solved in computer vision. Semantic segmentation is the challenge of classifying several pixels of an image into one category. With the help of convolution neural networks, we have witnessed prolific results over the time. We propose a convolutional neural network model which uses Fully CNN with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view data set. Our method achieves an mIoU accuracy of 92.04 %.