• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.967-970
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• 2020

In a previous work we have done, we presented a monitoring system to automatically detect some dogs' behaviors from videos. However, the input video data used by that system was pre-trimmed to ensure it contained a dog only. In a real-life situation, the monitoring system would continuously receive video data, including frames that are empty and ones that contain people. In this paper, we propose a YOLOv4-based system for automatic object detection and trimming of dog videos. Sequences of frames trimmed from the video data received from the camera are analyzed to detect dogs and people frame by frame using a YOLOv4 model, and then records of the occurrences of dogs and people are generated. The records of each sequence are then analyzed through a rule-based decision tree to classify the sequence, forward it if it contains a dog only or ignore it otherwise. The results of the experiments on long untrimmed videos show that our proposed method manages an excellent detection performance reaching 0.97 in average of precision, recall and f-1 score at a detection rate of approximately 30 fps, guaranteeing with that real-time processing.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.704-707
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• 2021

Pigs' aggressive behavior represents one of the common issues that occur inside pigpens and which harm pigs' health and welfare, resulting in a financial burden to farmers. Continuously monitoring several pigs for 24 hours to identify those behaviors manually is a very difficult task for pig caretakers. In this study, we propose a lightweight video-based approach for monitoring pigs' aggressive behavior that can be implemented even in small-scale farms. The proposed system receives sequences of frames extracted from an RGB video stream containing pigs and uses MnasNet with a DM value of 0.5 to extract image features from pigs' ROI identified by predefined annotations. These extracted features are then forwarded to a lightweight LSTM to learn temporal features and perform behavior recognition. The experimental results show that our proposed model achieved 0.92 in recall and F1-score with an execution time of 118.16 ms/sequence.

• 한국정보전자통신기술학회논문지
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• 제6권2호
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• pp.81-87
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• 2013

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2013년도 하계학술대회
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• pp.12-13
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• 2013

본 논문은 비가 오는 장면을 촬영한 동영상에서 빗줄기를 효과적으로 제거하는 기법을 제안한다. 제안하는 기법에서는 광흐름 검출 기법을 이용하여 인접한 프레임에서 현재 프레임의 픽셀에 대응하는 픽셀을 검출하고, 확률에 따라 해당 프레임의 픽셀 적용 유무를 결정한다. 빗줄기로 검출된 픽셀을 인접한 프레임의 픽셀 값으로 대체함으로써 영상 내 빗줄기를 제거한다. 컴퓨터 모의실험을 통해 제안하는 알고리즘이 동영상에서 효과적으로 빗줄기를 제거할 수 있음을 확인한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.153-156
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• 2003

This paper proposes a video tracking method for a deformable moving object using an active contour model. In order to decide the convergent directions of the contour points automatically, a new energy function based on a frame difference map and an updating rules of the frame difference map are presented. Experimental results on a set of synthetic and real image sequences showed that the proposed method can fully track a speedy deformable object while extracting the boundary of the object exactly in every frame.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.141-144
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• 2002

This paper describes a new feature based tracking system that can track moving objects with a pan-tilt camera. We extract corner features of the scene and tracks the features using filtering, The global motion energy caused by camera movement is eliminated by finding the maximal matching position between consecutive frames using Pyramidal template matching. The region of moving object is segmented by clustering the motion trajectories and command the pan-tilt controller to follow the object such that the object will always lie at the center of the camera. The proposed system has demonstrated good performance for several video sequences.

• 전자공학회논문지B
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• 제31B권12호
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• pp.59-71
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• 1994

This paper proposes a motion estimation method in video sequences based on the feature based matching and anistropic propagation. It measures translation and rotation parameters using a relaxation scheme at feature points and object orinted anistropic propagation in continuous and discontinuous regions. Also an iterative improvement motion extimation based on the strongly coupled module fusion and adaptive smoothing is proposed. Computer simulation results show the effectiveness of the proposed algorithm.

• 한국정보전자통신기술학회논문지
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• 제7권1호
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• pp.61-67
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• 2014

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter(AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.137-140
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• 2000

본 논문에서는 카메라의 움직임이 있는 비디오 영상에서 물체의 움직임을 검출하기 위한 방법을 제안한다. 입력 영상에서 카메라의 움직임이 있을 경우, 이를 제거하기 위해 카메라의 움직임을 추정, 보상한다. 보상된 영상에서 적응적 임계값을 사용하여 물체의 움직임을 검출한다. 제안된 방법은 카메라의 움직임이 있는 비디오 영상뿐만 아니라, 시간에 따라 조명이 변하거나 잡음이 포함된 비디오 영상에 대해 좋은 결과를 얻을 수 있다.

• 방송공학회논문지
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• 제19권2호
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• pp.250-256
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• 2014

현재 MPEG에서 무료(royalty-free) 비디오 코덱으로 표준화 중인 IVC(Internet Video Coding)에서는 저지연(low-delay) 모드 부호화 구조에서 비참조 P 프레임 부호화 기법을 사용하여 부호화 성능 이득을 얻고 있다. IVC 시험모델 ITM(IVC Test Model) 4.0에 채택되어 있던 기존의 비참조 P 프레임 기법은 움직임 벡터(MV)의 크기를 이용하여 적응적으로 고정된 부호화 구조의 비참조 P 프레임을 적용하였으나 시퀀스에 따라서 오히려 부호화 성능이 크게 떨어지는 단점이 있었다. 본 논문에서는 이러한 기존의 적응적 비참조 P 프레임 부호화 성능을 개선하기 위하여 기존의 고정된 비참조 P 프레임의 구조를 변경하는 기법과 MV와 함께 참조 프레임 대비 비참조 프레임의 발생 비트량 비를 함께 사용하는 적응적 기법을 제시한다. 실험결과 제안된 기법은 시퀀스에 따른 큰 성능 저하 없이 ITM 7.0 대비 6.6% 정도의 비트율 감소를 얻음을 확인하였다.