• International Journal of Contents
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• 제10권1호
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• pp.1-11
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• 2014

In the current generation of smart mobile devices, object tracking is one of the most important research topics for computer vision. Because human face tracking can be widely used for many applications, collecting a dataset of face videos is necessary for evaluating the performance of a tracker and for comparing different approaches. Unfortunately, the well-known benchmark datasets of face videos are not sufficiently diverse. As a result, it is difficult to compare the accuracy between different tracking algorithms in various conditions, namely illumination, background complexity, and subject movement. In this paper, we propose a new dataset that includes 91 face video clips that were recorded in different conditions. We also provide a semi-automatic ground-truth generation tool that can easily be used to evaluate the performance of face tracking systems. This tool helps to maintain the consistency of the definitions for the ground-truth in each frame. The resulting video data set is used to evaluate well-known approaches and test their efficiency.

• 한국정보통신학회논문지
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• 제9권2호
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• pp.277-282
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• 2005

VBR (variable bit rate) MPEG 비디오 트래픽은 COP(group of pictures)의 시작인 I-픽쳐에서는 다른 픽쳐들보다 매우 큰 양의 트래픽이 발생하기 때문에 COP 구조에 따라 주기적 형태의 트래픽 발생 패턴을 가진다. 따라서, VBR MPEG 비디오 정보원이 다중화 될 때 I-픽쳐들의 시작 시간 배열은 다중화기의 셀 손실 특성에 큰 영향을 준다. 본 논문에서는 VBR MPEG 비디오 정보원들이 하나의 전송로로 전송되기 위해 다중화 될 때 다중화기에서의 셀 손실률을 최소화하기 위해서 각 비디오 정보원의 I-픽쳐 시작시간들을 배열하는 방안을 제시한다. 제안하는 방안에서는 정확한 I-픽쳐 시작 시간을 효과적으로 찾기 위해 다중화된 정보원의 셀 도착률이 전송로의 용량을 초과하는 확률을 이용하였다. 모의 실험을 통해 제안하는 방법이 기존의 방법들 보다 최적으로 비디오 정보원들을 다중화 시키는 것을 보였다.

• 한국대기환경학회지
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• 제34권5호
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• ETRI Journal
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• 제35권6호
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• 한국주거학회논문집
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• 제26권1호
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• pp.91-98
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• 2015

The purposes of this study are to analyze the problems of the elderly using kitchens by ergonomic work evaluation methods, and to make suggestions for planning and remodeling of the kitchens for the elderly. The work evaluation methods which used in this study were direct-observing methods, which contained the process of 2 or 3 times each visiting to four different houses where elderly people live. For direct-observing methods, analyzing with movement observations and observation methods with photos and videos were used. Characteristics of subject elderly people and problems of their kitchens were analyzed by static measurements, interviews, pictures, and videos. The data, which are recorded movements of the preparing meal of the elderly were analyzed by playing it back repeatedly. As results, physical characteristics of the elderly was the most important consideration; a participant with the arthritical knee was limping at the kitchen entrance due to the difference of the floor level, and a user with a bent back was working on the floor or place an elbow on the worktable to support her body. Those results made a conclusion about the common problems of the kitchens, and suggested the check list which has to be considered when designing the elderly using kitchen.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 2부
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• pp.446-450
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• 2008

테크놀로지의 발달로 전통적 공연예술 분야 에서도 디지털 미디어를 이용하여 표현영역을 확대하려는 시도가 증가하고 있다. 기존 공연은 영상을 단기 공연의 배경 정도로 활용하는 경우가 대부분이었으나, '미키쥐의 죽음' 에서는 이러한 기존공연의 표현 영역을 탈피하여, 미디어 테크놀러지를 이용한 영상, 퍼포머, 소리가 유기적으로 융합된 인터랙티브 퍼포먼스를 구현하였다. 이 결과, 미디어와 퍼포머 사이의 상호작용성 증가로 무대라는 연출된 공간에 더욱 큰 리얼리티를 부여하여 현장감이 강화되고, 표현 영역이 확장 되는 새로운 공연양식의 발전 가능성을 제시하였다.

• 산업기술연구
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• 제38권1호
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• pp.41-52
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• ETRI Journal
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• 제31권1호
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• pp.1-9
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• 2009

Frequency domain analysis is a fundamental procedure for understanding the characteristics of visual data. Several studies have been conducted with 2D videos, but analysis of stereoscopic 3D videos is rarely carried out. In this paper, we derive the Fourier transform of a simplified 3D video signal and analyze how a 3D video is influenced by disparity and motion in terms of temporal aliasing. It is already known that object motion affects temporal frequency characteristics of a time-varying image sequence. In our analysis, we show that a 3D video is influenced not only by motion but also by disparity. Based on this conclusion, we present a temporal anti-aliasing filter for a 3D video. Since the human process of depth perception mainly determines the quality of a reproduced 3D image, 2D image processing techniques are not directly applicable to 3D images. The analysis presented in this paper will be useful for reducing undesirable visual artifacts in 3D video as well as for assisting the development of relevant technologies.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.207-210
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• 2007

As the Internet is prevalent in our life, harmful contents have been increasing on the Internet, which has become a very serious problem. Among them, pornographic video is harmful as poison to our children. To prevent such an event, there are many filtering systems which are based on the keyword based methods or image based methods. The main purpose of this paper is to devise a system that classifies the pornographic videos based on the audio information. We use Mel-Cepstrum Modulation Energy (MCME) which is modulation energy calculated on the time trajectory of the Mel-Frequency cepstral coefficients (MFCC) and MFCC as the feature vector and Gaussian Mixture Model (GMM) as the classifier. With the experiments, the proposed system classified the 97.5% of pornographic data and 99.5% of non-pornographic data. We expect the proposed method can be used as a component of the more accurate classification system which uses video information and audio information simultaneously.

• 한국인터넷방송통신학회논문지
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• 제18권2호
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• pp.11-17
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• 2018

본 연구는 국내에서 제작된 인기 있는 YouTube 채널들의 특징들을 탐구한다. 과학과 기술이 발달함에 따라, 일반인들도 동영상을 촬영한 뒤, 자신의 영상을 업로드를 할 수 있는 상황으로 변화였다. 전 세계적으로 인기 있는 동영상 제공 서비스 사이트 YouTube에서, 국내에서 인기 높은 YouTube 채널들의 특징들을 조사한다. 구체적으로, 1) 구독자 2) 시청 항목의 인기 채널 100개를 조사 한 뒤, 1) 가장 높은 구독자/시청 숫자 2) 비디오 개수 3) 시청/구독자 숫자 4) 장르 5) 제작자로 나누어 탐구한다. 이를 통해, 한국에서 인기 있는 YouTube 채널들의 다각적인 모습뿐만 아니라 한국 멀티채널네트워크(MCN) 시장을 전망한다.