• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.37-43
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• 2016

This paper introduces a method for video shot group detection needed for efficient management and summary of video. The proposed method detects shots based on low-level visual properties and performs temporal and spatial clustering based on visual similarity of neighboring shots. Shot groups created from temporal clustering are further clustered into small groups with respect to visual similarity. A set of representative shot frames are selected from each cluster of the smaller groups representing a scene. Shots excluded from temporal clustering are also clustered into groups from which representative shot frames are selected. A number of video clips are collected and applied to the method for accuracy of shot group detection. We achieved 91% of accuracy of the method for shot group detection. The number of representative shot frames is reduced to 1/3 of the total shot frames. The experiment also shows the inverse relationship between accuracy and compression rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.145-148
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• 2007

The video shot detection based on video picture type is presented in this paper. The detection algorithm is used MPEG compressed video frame directly, not reconstructed the original image. For shot detection, I and P frame of MPEG video bit stream are classified. The detecting scene cuts at I pictures are detected by reconstructed DC image. While scene cuts at P picture frame by monitoring the percentage of Intra-macroblocks per P picture. Experimental results on the test video bit stream is shown the detection rate of $85\sim98%$ and searching time is 4 times faster than the previously known video shot detection algorithm on the decompressed video shot.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.269-272
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• 2007

The shortcut shot detection based on MPEG compressed video bitstream is presented in this paper. The detection algorithm is used the video picture frame from MPEG compressed video directly not to be decompressed the original image. For shortcut detection, I and P frame of MPEG video bitstream are classified. The changing scene cuts at I pictures are detected by the decompressed DC image and scene cuts at P picture frame by monitoring the percentage of intra-macroblocks per P picture are detected. Experimental results using test video bitstream QVGA results in average 92% detection rate, searching time is taken around 4.5 times faster in comparison with changing scene shot detection algorithm which is decompressed the compressed bitstream.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.229-232
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• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.304-308
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• 2001

The increase of video data makes the demand of efficient retrieval, storing, and browsing technologies necessary. In this paper, a video segmentation method (scene change detection method, or shot boundary detection method) for the development of such systems is proposed. For abrupt cut detection, inter-frame similarities are computed using luminance and edge histograms and a cut is declared when the similarities are under th predetermined threshold values. A gradual scene change detection is based on the similarities between the current frame and the previous shot boundary frame. A correlation method is used to obtain universal threshold values, which are applied to various video data. Experimental results show that propose method provides 90% precision and 98% recall rates for abrupt cut, and 59% precision and 79% recall rates for gradual change.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.582-592
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• 2001

Video indexing plays an important role in the applications such as digital video libraries or web VOD which archive large volume of digital videos. Video indexing is usually based on video segmentation. In this paper, we propose a software tool called V2Web Studio which can generate video clips utilizing shot boundary detection algorithm. With the V2Web Studio, the process of clip generation consists of the following four steps: 1) Automatic detection of shot boundaries by parsing the video, 2) Elimination of errors by manually verifying the results of the detection, 3) Building a modeling structure of logical hierarchy using the verified shots, and 4) Generating multiple video clips corresponding to each logically modeled segment. The aforementioned steps are performed by shot detector, shot verifier, video modeler and clip generator in the V2Web Studio respectively.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.59-66
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• 2004

In this paper, we propose a transmission rate prediction method of video data. The proposed method checks shot transition characteristics after dividing MPEG video data into a GoP unit and then uses Kalman filter. It used algorithm to detect shot transition information by high speed in compressed domain in order to check a correct shot transition of video data and classified into a abrupt shot transition type and a gradual shot transition type. Information to have been classifying is used as factors of Kalman filter and predicts a transmission rate of video data. Also, the proposed method decreased processing time with detecting shot transition and predicting a transmission rate of video data in compressed domain. It predicted a transmission rate with 96.2- 97.6% in the experiment that used three kinds of 911 1frames of different video data.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.111-119
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• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with $\chi2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• 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%.