• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제17권1호
• /
• pp.47-54
• /
• 2013

This work deals with automatic motion detection for with surveillance tracking that aims to provide high-lighting movable objects which is discriminated from moving backgrounds such as moving trees, etc. For this aim, we perform a false background region detection together with an initial foreground detection. The false background detection detects the moving backgrounds, which become eliminated from the initial foreground detection. This false background detection is done by performing the bimodal segmentation on a deformed image, which is constructed using the information of the dominant colors in the background.

• 한국멀티미디어학회논문지
• /
• 제17권4호
• /
• pp.420-432
• /
• 2014

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. 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 corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2009년도 추계학술대회
• /
• pp.574-578
• /
• 2009

정지 영상이나 비디오 영상 시퀀스에서 배경 영상으로부터 움직이는 관심 물체를 구별하기 위한 실시간 물체 검출은 물체의 위치 추적과 인식에 있어 필수적인 단계이다. 물체 분할 후에 그림자 영역이 움직이는 물체 영역에 포함되어지기 때문에 그림자는 물체의 일부분 혹은 움직이는 물체로 오분류될 수 있다. 이러한 이유로 그림자 제거 알고리즘은 움직이는 물체 검출 및 추적 시스템의 결과에 중요한 역할을 한다. 이 문제점들을 해결하기 위해 본 논문에서는 움직이는 물체의 특징과 색상공간에서 그림자의 특징에 기반을 둔 정확한 물체 검출과 그림자 제거 알고리즘을 제안한다. 실험결과는 제안 알고리즘이 실험 영상에서 물체 검출과 그림자 제거에 대해 효과적인 것을 알 수가 있다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2007년도 하계종합학술대회 논문집
• /
• pp.283-284
• /
• 2007

This paper proposes a method of real-time segmentation of moving region and detection of head position in a single omnidrectional camera Segmentation of moving region used background modeling method by a mixture of Gaussian(MOG) and shadow detection method. Circular constraint was proposed for detecting head position.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
• /
• pp.163-166
• /
• 2000

Real-time traffic detection scheme based on Computer Vision is capable of efficient traffic control using automatically computed traffic information and obstacle detection in moving automobiles. Traffic information is extracted by segmenting vehicle region from road images, in traffic detection system. In this paper, we propose the advanced segmentation of vehicle from road images using multiple local region information. Because multiple local region overlapped in the same lane is processed sequentially from small, the traffic detection error can be corrected.

• 전자공학회논문지S
• /
• 제35S권8호
• /
• pp.55-67
• /
• 1998

본 논문에서는 컬러 비디오 시퀀스 상에서 눈과 입에 해당하는 얼굴 특징점을 고속으로 추출하는 방법을 제안한다. 자유로운 움직임을 갖는 얼굴 영역을 안정적으로 추출하기 위해 얼굴 색상 분포를 이용한 색상 변환 영상에 움직임 검출 기법을 적용하여 움직이는 살색 부분만을 효율적으로 검출하는 색상 움직임 개념을 사용하였다. 움직임 정보는 살색의 가능성 정도에 따라 가중치가 주어지며 화소 단위의 움직임 여부를 결정하는 문턱값도 살색의 가능성 정도에 따라 적응적으로 결정된다. 눈의 색상분포와 형태소 연산자를 사용한 움직임 살색 영역에서 눈 후보 영역을 추출하고 눈과 눈썹의 상호 위치 관계를 이용하여 눈의 영역을 최종 결정한다. 입의 영역은 눈의 위치를 기준으로 입 후보 영역을 정하고 색상 히스토그램을 이용하여 입의 영역을 검출한다. 찾아진 눈과 입의 영역에서 정확한 특징점의 위치를 구하기 위해 PCA (Principal Component Analysis)를 사용하였다. 실험 결과 복잡한 배경, 개인적인 편차, 얼굴의 방향과 크기 등에 영향을 받지 않고 고속으로 정확한 얼굴의 특징점을 추출할 수 있었다.

• 디지털콘텐츠학회 논문지
• /
• 제6권3호
• /
• pp.157-162
• /
• 2005

본 논문에서는 2차원 정지 영상 및 동영상에서 블록 정합을 이용한 움직임 검출과 영역 분할을 통하여 생성한 깊이 지도를 이용하여 입체 영상으로 실시간 변환하는 방법을 제안하였다. 성능 평가는 움직임 객체의 깊이 지도와 절대 시차 차이 영상을 생성하여 기존의 변환 방법과 비교를 통해 제안한 방식의 우수성을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권11호
• /
• pp.5624-5638
• /
• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• 한국컴퓨터정보학회논문지
• /
• 제22권3호
• /
• pp.45-51
• /
• 2017

In this paper, we propose a consecutive-frame super-resolution method to tackle a moving object problem. The super-resolution is a method restoring a high resolution image from a low resolution image. The super-resolution is classified into two types, briefly, single-frame super-resolution and consecutive-frame super-resolution. Typically, the consecutive-frame super-resolution recovers a better than the single-frame super-resolution, because it use more information from consecutive frames. However, the consecutive-frame super-resolution failed to recover the moving object. Therefore, we proposed an improved method via moving object detection. Experimental results showed that the proposed method restored both the moving object and the background properly.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권2호
• /
• pp.118-123
• /
• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.