• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.593-603
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• 2009

In this paper, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with an active stereo camera. The proposed system consists of three modules: detection, tracking and silhouette extraction. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap is estimated in advance and then this was effectively incorporated into the graph cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data and it was shown to detect and track multiple people very well and also produce high quality silhouettes. The proposed system can assist in gesture and gait recognition in field of Human-Robot Interaction (HRI).

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.289-292
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• 2008

본 논문에서는 사용자에게 보다 효율적이고 직관적인 비디오 브라우징 서비스를 제공하기 위하여 비디오 데이터의 종류와 특성에 제한받지 않고 강건하게 적용될 수 있는 장면전환 검출 알고리즘을 제안하고자 한다. 제한된 알고리즘은 명암 값의 급 변화나 객체의 빠른 움직임, 영상의 왜곡 등에 의한 장면전환 검출의 오류를 제거할 수 있으며, 특히 연속된 프레임사이의 강건한 차이 값 추출을 위한 개선된 식을 제안하고, 추출된 차이 값들로부터 변화패턴을 학습하고 특징을 추출함으로서 자동 임계치 결정에 활용하였다. 제안된 방법은 급진적인 장면변화가 많고 플래시라이트와 같은 조명의 변화가 많은 다양한 비디오 데이터를 가지고 실험되었으며, 실험결과 기존의 방법에 비교하여 효율적이고 신뢰할 수 있는 결과 값들을 보여주었다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.49-52
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• 2006

오늘날 컴퓨터의 발달과 인터넷의 확산으로 멀티미디어 컨텐츠의 보급이 급격히 확대되고 있으며, 이들 컨텐츠에는 원거리 화상회의, 감시시스템, 주문형 비디오(VOD), 주문형 뉴스(NOD), 디지털 편집 시스템 등 동영상이 포함되어 있다. 이처럼 동영상은 정보교환과 정보표현의 매개물로서 중요한 역할을 한다. 그러나 이와 같은 동영상은 노이즈나 전송과정 중 발생하는 문제 등으로 인해 항상 좋은 품질을 보장되지 않는다. 이런 훼손된 영상을 원영상으로 복원하거나 사용자가 제거 혹은 복원하고자 하는 영역을 지정 처리함으로서 다양한 정보를 획득할 수 있다. 일반적으로 pc에서 사용되어지는 대부분의 동영상은 $15fps{\sim}30fps$이다. 대부분의 동영상 편집 기술은 각각의 frame을 추출하여 수동적으로 처리하므로 비용과 시간이 많이 든다. 이런 단점을 해결하기 위해 여러 방법이 기존에 시도되고 있다. 제거 혹은 복원하고자 하는 영역을 전 frame에서 처리하기 위해 움직임 검출 및 추적기법이 사용되며, 제거 혹은 복원하기 위해 median filtering, image inpainting 처리 방법들이 있다. 본 연구에서는 사용자에 의해 미리 정의된 바운딩 박스내의 객체를 추적하여 객체의 중심값을 찾는 mean-shift algorithm을 이용하여 움직이는 객체를 추적하였고 image Inpainting algorithm을 이용하여 훼손된 영역을 복원하거나 제거하고자 하는 객체를 제거하였다.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.58-66
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• 2020

The blur effect, which is generated by various external factors such as out-of-focus and object movement, degrades high-frequency components in the original sharp image. Based on this observation, we propose a novel method for blur detection using textural features. Specifically, the proposed method simultaneously adopts learning-based and watershed-based textural features, which effectively detect the blur on various situations. Moreover, we employ the region-based refinement to improve the processing time while also increasing detection accuracy. Experimental results demonstrate that the proposed method provides the competitive performance compared to previous approaches in literature.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.576-586
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• 2020

Analyzing baseball games using sensors such as radars or riders is expensive. In this paper, we propose an algorithm to detect pitch shots and hit shots using baseball video and to generate ball trajectories within hit shots using camera movement. After the pitch shot and the hit shot detection using object detection and optical flow, we generate the transformation relationship between frames and ball locations in the frame, and calculates the ball trajectory. The performance of the proposed method is evaluated for three KBO baseball video sequences, and the detection accuracy and detection rate of pitch shot and hit shot were within 89-95 [%], and the average error for shot range was 13.6[m], The direction error was 7.5° and foul classification accuracy was 98.6%.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.27-34
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• 2013

Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.

• Spatial Information Research
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• v.21 no.3
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• pp.11-19
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• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.177-186
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• 2012

In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.

• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.400-405
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• 2006

In this paper, we propose a moving object tracking algorithm by using motion estimation in UAV(Unmanned Aerial Vehicle) video. Proposed algorithm is based on generation of initial image from detected reference image, and tracking of moving object under the time-varying image. With a series of this procedure, tracking process is stable even when the UAV camera sways by correcting position of moving object, and tracking time is relatively reduced. A block matching algorithm is also utilized to determine the similarity between reference image and moving object. An experimental result shows that our proposed algorithm is better than the existing full search algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.778-786
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• 2002

Scene boundary detection is important in the understanding of semantic structure from video data. However, it is more difficult than shot change detection because scene boundary detection needs to understand semantics in video data well. In this paper, we propose a new approach to scene segmentation using contextual information in video data. The contextual information is divided into two categories: local and global contextual information. The local contextual information refers to the foreground regions' information, background and shot activity. The global contextual information refers to the video shot's environment or its relationship with other video shots. Coherence, interaction and the tempo of video shots are computed as global contextual information. Using the proposed contextual information, we detect scene boundaries. Our proposed approach consists of three consecutive steps: linking, verification, and adjusting. We experimented the proposed approach using TV dramas and movies. The detection accuracy of correct scene boundaries is over than 80%.