• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.811-819
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• 2017

A behavior pattern analysis algorithm based on descriptors consists of information of a moving object and temporal histogram is proposed. Background learning is performed first for detecting, tracking and analyzing moving objects. Each object is identified using an association of the center of gravity of objects and tracked individually. A temporal histogram represents a motion pattern using positions of the center of gravity and time stamp of objects. The characteristic and behavior of objects are figured out by comparing each coordinates of a position history in the histogram. Behavior information which is comprised with numbers of a start and end frame, and coordinates of positions of objects is stored and managed in the linked list. Descriptors are made with the stored information and the video retrieval algorithm is designed. We confirmed the higher retrieval accuracy compare with conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.13-22
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• 2004

Because video sequence consists of dynamic objects in nature, the object motion in video is an effective feature in describing the contents of video sequence and motion feature plays an important role in video retrieval. In this paper, we propose a method that converts motion vectors (MVs) to a uniform set on MPEG coded domain, independent of the frame type and the direction of prediction, and utilizes these normalized MVs (N-MVs) as motion descriptor to understand video contents. We describe a frame-type independent representation of the various types of frames presented in an MPEG video in which all frames can be considered equivalently, without full-decoding. In the experiments, we show that the proposed method is better than the conventional one in terms of performance.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.461-465
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• 2002

현재 모션 캡쳐 데이터는 파일 형태로 존재하며 이들 파일을 관리하기 위해서 문서관리시스템 등을 사용할 수 있다. 그러나 파일 이름만으로는 원하는 모션 데이터를 가지고 있는 모션 캡쳐파일을 검색하기 어렵다. 본 논문에서는 인체 바디 모션을 표현하는 마크업 언어인BAML(Body Animation Markup Language)을 이용하여 모션 캡쳐 파일에 모션 정보를 추가적으로 표현함으로써 모션 캡쳐 파일을 애니메이션 소프트웨어로 실행하기 전에 모션 파일 라이브러리에서 특정 모션을 가지고 있는 모션 캡쳐 데이터 파일을 검색할 수 있는 BAML+ 시스템을 제시하고자 한다. BAML+ 시스템은 모션 캡쳐 데이터의 모션명을 정의하고 모션 변형 시스템에서 모션을 검색하고 편집할 수 있는 컴포넌트들로 구성된다.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.102-112
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• 2000

The previous video structuring techniques are mainly limited to shot or shot group level. However, the shot level structure couldn't provide semantics within a video. So, researches on high level structuring are going on for getting over the drawbacks of shot level structure, recently. To overcome the drawbacks of shot level structure, we propose video scene segmentation technique based on color and motion features. For considering various color distribution, each shot is divided into sub-shots based on color feature. A key frame is extracted from each sub-shot. The motion feature in a shot is extracted from MPEG-1 video's motion vector. Moreover adaptive weights based on motion's property in search range are applied to color and motion features. The experiment results of proposed technique show the excellence in view of the over-segmentation and the reflection of semantics, comparing with those of previous techniques. The proposed technique decomposes video into meaningful hierarchical structure and provides video browsing or retrieval based on scene.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.510-520
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• 2000

For content-based video indexing and retrieval, it is necessary to segment video data into video shots and then select key frames or representative frames for each shot. However, it is very difficult to select key frames automatically because the task of selecting meaningful frames is quite subjective. In this paper, we propose a new approach in selecting key frames based on visual contents such as region information and their temporal variations in the shot. First of all, we classify video shots into panning shots, zooming shots, tilting shots or no camera motion shots by detecting camera motion information in video shots. Then, in each category, we apply appropriate fuzzy rules to select key frames based on meaningful content in frame. Finally, we control the number of key frames in the selection process by adjusting the degree of detail in representing video shots.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.121-132
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• 2000

Most of video indexing applications depend on fast and efficient archiving, browsing, retrieval techniques. A number of techniques have been approached about only pixel domain analysis until now. Those approaches brought about the costly overhead of decompressing because the most of multimedia data is typically stored in compressed format. But with a compressed video data, if we can analyze the compressed data directly. then we avoid the costly overhead such as in pixel domain. In this paper, we analyze the information of compressed video stream directly, and then extract the available features for video indexing. We have derived the technique for cut detection using these features, and the stream is divided into shots. Also we propose a new brief key frame selection technique and an efficient video indexing method using the spatial informations(DT coefficients) and also the temporal informations(motion vectors).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8649-8653
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• 2015

A number of video indexing and retrieval algorithms have been proposed to manage large video databases efficiently. The video similarity measure is one of most important technical factor for video content management system. In this paper, we propose the luminance characteristics model to measure the video similarity efficiently. Most algorithms for video indexing have been commonly used histograms, edges, or motion features, whereas in this paper, the proposed algorithm is employed an efficient similarity measure using the luminance projection. To index the video sequences effectively and to reduce the computational complexity, we calculate video similarity using the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed luminance projection model yields the remarkable improved accuracy and performance than the conventional algorithm such as the histogram comparison method, with the low computational complexity.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.255-261
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• 2014

Advances of digital device technology such as digital cameras, smart phones and tablets, provide convenience way for people to take pictures during his/her life. Photo data is being spread rapidly throughout the social network, causing the excessive amount of data available on the internet. Photo retrieval is categorized into three types, which are: keyword-based search, example-based search, visualize query-based search. The commonly used multimedia search methods which are implemented on Smart TV are adapting the previous methods that were optimized for PC environment. That causes some features of the method becoming irrelevant to be implemented on Smart TV. This paper proposes a novel Visual Query-based Photo Retrieval Method in Smart TV Environment using a motion sensing input device known as Kinect Sensor. We detected hand gestures using kinect sensor and used the information to mimic the control function of a mouse. The average precision and recall of the proposed system are 81% and 80%, respectively, with threshold value was set to 0.7.

• Archives of design research
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• v.19 no.2 s.64
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• pp.313-322
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• 2006

In this paper, we introduce 'Contents Based Image Retrieval' which helps a user find the images he or she needs more easily and reconfigures the images automatically. With this system, we try to realize the language of (motion) picture, that is, the Montage from the viewpoint of the user. The Real-time Montage System introduced in this paper uses 'Discrete Fourier Transform'. Through this, the user can find the feature of the image selected and compare the analogousness with the image in the database. This kind of system leads to the user's speedy and effective retrieving, Also, we can acquire the movement image of the user by Camera Tracking in Real-time. The movement image acquired is to be reconfigured automatically with the image of the user. In this way, we can get an easy and speedy image reconfiguration which sets to the user's intention. This system is a New Media Design tool(entertainment) which induces a user enjoy participating in it. In this system, Thus, the user is not just a passive consumer of one-way image channels but an active subject of image reproduction in this system. It is expected to be a foundation for a new style of user-centered movie (media based entertainment).

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.1-11
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• 2008

This paper detected the characteristics of motion vector to support efficient content -based video search of video. Traditionally, the present frame of a video was divided into blocks of equal size and BMA (block matching algorithm) was used, which predicts the motion of each block in the reference frame on the time axis. However, BMA has several restrictions and vectors obtained by BMA are sometimes different from actual motions. To solve this problem, the foil search method was applied but this method is disadvantageous in that it has to make a large volume of calculation. Thus, as an alternative, the present study extracted the Spatio-Temporal characteristics of Motion Vector Spatio-Temporal Correlations (MVSTC). As a result, we could predict motion vectors more accurately using the motion vectors of neighboring blocks. However, because there are multiple reference block vectors, such additional information should be sent to the receiving end. Thus, we need to consider how to predict the motion characteristics of each block and how to define the appropriate scope of search. Based on the proposed algorithm, we examined motion prediction techniques for motion compensation and presented results of applying the techniques.