• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.101-103
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• 2013

Most pickpockets occurs at a place where a lot of people. However, the current occurs more commonly in a secluded place and unfrequented place. In this paper, we classified to the scene for submitting to image forensics evidence target for pickpockets of theft. Using the ${\chi}^2$ histogram to detect the scene change detection. We wish to submit evidence by classifying as a pickpocket scene video.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.605-608
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• 2002

본 논문에서는 동영상에서 샘플 프레임과 주변 화소의 컬러 히스토그램을 이용한 새로운 장면 전환 방법을 제시하였다. 4개의 대표 컬러에 대한 주변 화소의 컬러 히스토그램을 이용하여 장면 전환 검출을 함으로써 기존의 컬러 히스토그램을 이용한 장면 전환 검출의 단점을 보완하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.609-613
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• 2002

본 논문에서는 효과적인 비디오 인덱싱을 위해 얼굴 영역 추출을 통한 장면 전환 검출 방법을 제시하였다. 히스토그램과 사람의 피부색 검출을 통해 사람의 얼굴을 포함하는 후보 프레임을 찾고, 얼굴 영역과 특징 추출을 통해 사람을 포함하는 키 프레임을 검출하여 비디오의 장면 전환 프레임을 검출하고, 실험을 통해 제안된 방법의 우수성을 보였다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.273-273
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• 2004

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.65-74
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• 2005

This Paper Propose a robust scene change detection technique that use the weighted chi-square test and the automated threshold-decision algorithm. The weighted chi-test can subdivide the difference values of individual color channels by calculating the color intensities according to mSC standard, and it can detect the scene change by joining the weighted color intensities to the predefined chi-test which emphasize the comparative color difference values. The automated decision algorithm uses the difference values of frame-to-frame that was obtained by the weighted chi-test. In the first step, The average of total difference value and standard deviation value is calculated and then, subtract the mean value from the each difference values. In the next step, the same process is performed on the remained difference value. The propose method is tested on various sources and in the experimental results, it is shown that the Proposed method is efficiently estimates the thresholds and reliably detects scene changes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.85-91
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• 2011

The technique, which is able to detect and recognize characters in videos such as a movie or TV drama, can be used for applications which are database management of a general user's facial images for the suppliers of PVR(personal video recorder), mobile phones, and multimedia, etc. In this paper, we propose a face detection algorithm. It searches the character through cast indexing when the scene is changed in video. It is consisted of three stages. The first step is the detection-step of the scene change after producing a paused image. The second step is the face detection-step using color information. The final step is the detection-step which detects its features by the facial boundary. According to the experimental result, it has detected faces in different conditions successfully and more advanced than the existing other one that are using only color information.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.162-165
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• 2018

In this paper, we propose a pre-processing procedure to obtain scene change information using spatial down-scaled input image for efficient encoding of super-high resolution image and propose a reconstruction of reference picture list in inter-picture prediction using this information. The experimental results show that the proposed method improves the BD-Rate by 0.44% and reduces encoding time by 12.46% when compared to HM 16.12.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.629-634
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• 2008

디지털 영상 매체들이 등장한 후부터 장면전환 검출은 영상의 편집과 검색, 요약 등 여러 작업에 적용되기 위해 활발히 연구되어 왔다. 특히 디지털 방송이 MPEG2방식으로 송신되기 시작한 이후로 이러한 연구는 더욱 활발히 진행되었다. 그 결과로 MPEG2 영상에서 장면전환을 검출하기 위해서 압축영역에서의 검출법과 비압축영역에서의 검출법이 제시되었다. 특히 압축영역에서의 장면전환 검출방법은 전체를 디코딩하지 않고 장면전환을 빠르게 검색할 수 있는 방법들이 주로 등장되었다. 하지만, 이 방법들은 정확도가 떨어지거나 속력저하가 극심한 등 여러 가지 문제를 보였다. 따라서 우리는 좀 더 빠르고 정확도가 높은 장면전환 시점 검출을 위해서 GOP의 길이와 B 프레임의 용량 변화를 이용하고자 한다. 우리의 방법은 B 프레임의 용량 변화를 이용하여 장면 전환을 보다 빠르게 검색하고 보다 높은 정확도를 위해서 GOP 길이의 변화가 심한 곳을 추가로 지정하여 정확도를 보강한다. 이러한 방법은 기존의 장면전환 검출 방법보다 빠른 해결책이 된다. 그 뿐 아니라 정확도 면에서도 만족할만한 결과를 보여주고 있다. 본 논문에서 제시한 이러한 방법은 기존의 획일적인 방법에서 벗어나 MPEG2 영상내에서 좀 더 빠르고 정확한 장면검출을 위한 새로운 아이디어를 제공한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.758-766
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• 2003

This paper proposes a tree-based hierarchical image mosaicing system using camera and object parameters for efficient video database construction. Gray level histogram difference and average intensity difference are proposed for scene change detection of input video. Camera parameter measured by utilizing least sum of square difference and affine model, and difference image is used for similarity measure of two input images. Also, dynamic objects are searched by through macro block setting and extracted by using region splitting and 4-split detection methods. Dynamic trajectory evaluation function is used for expression of dynamic objects, and blurring is performed for construction of soft and slow mosaic image.

• Journal of the Korea Computer Industry Society
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• v.9 no.4
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• pp.149-156
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• 2008

There is the temporal correlation of a animation sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the scene change detection algorithm for block matching using the temporal correlation of the animation sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that the proposed algorithm has better detection performance, such as recall rate, then the existing method. The algorithm has the advantage of speed, simplicity and accuracy. In addition, it requires less amount of storage.