• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1470-1476
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• 2012

Performance of a video browser greatly depends on the performance of scene change detection for the efficient retrieval and storage of the video contents which are major data in a current contents management system. In this paper we propose a new scene change detection method using Mean Difference Histogram of each frame section which improves accuracy of scene change detection as well as reduces the frequency of miss detection and fault detection of gradual scene change which is one of critical problem of the conventional histogram-based techniques.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.890-902
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• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.

• Annual Conference of KIPS
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• 2021.11a
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• pp.723-726
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• 2021

현재 단일 이미지에서 Object Detection 성능은 매우 좋은 편이다. 하지만 동영상에서는 처리 속도가 너무 느리고 임베디드 시스템에서는 real-time이 힘든 상황이다. 연구 논문에서는 하이엔드 GPU에서 다른 기능 없이 YOLO만 구동했을 때 real-time이 가능하다고 하지만 실제 사용자들은 상대적으로 낮은 사양의 GPU를 사용하거나 CPU를 사용하기 때문에 일반적으로는 자연스러운 real-time을 하기가 힘들다. 본 논문에서는 이러한 제한점을 해결하고자 계산량이 많은 Object Detection model 사용을 줄이는 방안은 제시하였다. 현재 Video영상에서 Object Detection을 수행할 때 매 frame마다 YOLO모델을 구동하는 것에서 YOLO 사용을 줄임으로써 계산 효율을 높였다. 본 논문의 알고리즘은 카메라가 움직이거나 배경이 바뀌는 상황에서도 사용이 가능하다. 속도는 최소2배에서 ~10배이상까지 개선되었다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.9
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• pp.14-24
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• 2009

Memory cell array and peripheral circuits are designed for system on panel style frame buffer. Moreover, a parallel test methodology to test multiple blocks of memory cells is proposed to overcome low yield of system on panel processing technologies. It is capable of faster fault detection compared to conventional memory tests and also applicable to the tests of various embedded memories and conventional SRAMs. The various patterns of conventional test vectors can be used to enhance fault coverage. The proposed testing method is also applicable to hierarchical bit line and divided word line, one of design trends of recent memory architectures.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.281-284
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• 2003

In this study, we have proposed the method of moving area detection as the preprocessing step of moving object tracking system. First, we catch the two frames which are different at time in image sequence. We obtain the moving area by using their binary differential image. In differential image, the object area of previous and current frame is present. In the tracking system, the background is changed by camera motion. So, in this case we have to decide which moving area of object is current at time. We obtain the binary edge image of current frame by applying a threshold to the output of an edge detector. Then we performed logical AND operation between the edge image and differential image. As a result of this work moving area of object can be detected.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.889-894
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• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.

• Korean Journal of Microbiology
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• v.43 no.4
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• pp.317-320
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• 2007

In this paper, we have developed base-calling error detection program and algorithm which show the list of the genes or sequences that are suspected to contain base-calling errors. Those programs detect dubious bases in a few aspects in the process of microbial genome project. The first module detects base-calling error from the Phrap file by using contig assembly information. The second module analyzes frame shift mutation if it is originated from real mutation or artifact. Finally, in the case that there is control microbial genome annotation information, the third module extracts and shows the candidate base-calling error list by comparative genome analysis method.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.3
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• pp.296-301
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• 2005

This paper proposing the use of the Instantaneous reactive power method as a harmonic detection method for a single phase active filter system. This method is to detect harmonic components through d-q frame approach. The conventional use of d-q frame approach for a 3-phase system Is extended to the single phase system. The proposed system uses a multi-level inverter for harmonic compensation and the inverter is connected to the input side without using transformers. The proposed algorithm is verified by simulation and experiment.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.146-153
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• 1990

A new 3-D segmentation-based coding technique is proposed for transmitting the motion video with reasonablly acceptable quality even at a very low bit rate. Only meaningful motion areas are extracted by using two change detection masks and a current frame is directly segmented rather than a difference frame itself so that a good quality of image can be obtained at high compression ratios. Through the experiments, the sequence of Miss America is reconstructed with visually acceptable quality at the very high compression ratio of 360:1.