• Journal of Mechanical Science and Technology
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• 제15권3호
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• pp.300-308
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• 2001

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.119-126
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• 2003

The problem of tracking moving objects in a video stream is discussed in this pa-per. We discussed the popular technique of optical flow for moving object detection. Optical flow finds the velocity vectors at each pixel in the entire video scene. However, optical flow based methods require complex computations and are sensitive to noise. In this paper, we proposed a new method based on the Hough transform and on voting accumulation for improving the accuracy and reducing the computation time. Further, we applied the Boo-lean based edge detector for edge detection. Edge detection and segmentation are used to extract the moving objects in the image sequences and reduce the computation time of the CHT. The Boolean based edge detector provides accurate and very thin edges. The difference of the two edge maps with thin edges gives better localization of moving objects. The simulation results show that the proposed method improves the accuracy of finding the optical flow vectors and more accurately extracts moving objects' information. The process of edge detection and segmentation accurately find the location and areas of the real moving objects, and hence extracting moving information is very easy and accurate. The Combinatorial Hough Transform and voting accumulation based optical flow measures optical flow vectors accurately. The direction of moving objects is also accurately measured.

• 한국통신학회논문지
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• 제27권8A호
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• pp.822-828
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• 2002

다제약 접근기반 OF(optical flow) 평가기술이 이동 물체의 인식에 자주 이용되고 있다. 그러나 OF 평가시간 뿐만 아니라 오차 문제로 인하여 사용이 제한되고 있다. 본 논문에서는 sobel 에쥐 검출과 다제약 접근기반 OF를 이용하여 효율적으로 움직임 정보를 추출하는 방법을 제안한다. 먼저 에쥐 검출 후 차영상과 영역분할기법으로 영상열 내 이동물체를 검출하고 임계치 처리로 잡음에 의해 검출된 이동물체들을 제거한다. 그리고 OF 최적 제약선을 찾기 위한 CHT와 Voting 누적을 적용한다. 이때 에쥐 검출과 영역분할을 이용함으로써 연속하는 영상열 내에서 이동 물체를 찾기 위한 CHT 계산시간을 현저히 줄이는 것이 가능하다. CHT 기반의 Voting은 최소자승법을 가미함으로써 오차 또한 감소시킨다. 그리고 제약선에 따른 수많은 점들을 계산하는 작업도 변환된 기울기-교점 파라미터를 사용함으로써 줄어들게 된다. 시뮬레이션 결과 영상 내에서 이동물체 인식비가 증가됨을 보였고 이동물체의 움직임 정보를 제공하는 OF 벡터도 매우 효율적으로 검출됨을 확인하였다.

• 한국정보과학회논문지:데이타베이스
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• 제30권6호
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• pp.585-592
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• 2003

공간 데이타베이스 시스템에서는 동일한 레이어 상에서 보안등급이 다른 두개 이상의 비공간 정보로 이루어진 공간 객체를 관리할 필요성이 있다. 이러한 공간 객체 관리를 위해 관계 데이터베이스 시스템의 다중인스턴스화(polyinstantiation) 기법을 적용하면 공간 객체의 표현상 문제와 상이한 보안등급을 가지는 주체의 접근으로 인한 서비스 거부(service denial)와 정보 노출(information flow)이라는 문제가 발생한다. 본 논문에서는 이와 같은 문제점을 해결하기 위해 상이한 접근등급을 갖는 공간 객체를 위한 다중인스턴스화 기법을 제안한다. 제안된 기법은 공간 객체에 대해 보안등급 변환검사 단계와 다중인스턴스 생성단계를 통하여 사용자의 등급에 따라 새로운 공간 객체를 생성하고, 이를 보안 정책에 활용한다. 또한 상이한 등급의 사용자가 공간 객체에 대하여 다양한 보안 연산을 요구할 경우 발생하는 서비스 거부와 정보노출의 문제점을 각 등급에 따른 공간 객체 다중인스턴스를 생성하여 해결한다.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.65-71
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• 2024

An indirect visual SLAM takes raw image data and exploits geometric information such as key-points and line edges. Due to various environmental changes, SLAM performance may decrease. The main problem is caused by dynamic objects especially in highly crowded environments. In this paper, we propose a robust feature-based visual SLAM, building on ORB-SLAM, via multi-channel dynamic objects estimation. An optical flow and deep learning-based object detection algorithm each estimate different types of dynamic object information. Proposed method incorporates two dynamic object information and creates multi-channel dynamic masks. In this method, information on actually moving dynamic objects and potential dynamic objects can be obtained. Finally, dynamic objects included in the masks are removed in feature extraction part. As a results, proposed method can obtain more precise camera poses. The superiority of our ORB-SLAM was verified to compared with conventional ORB-SLAM by the experiment using KITTI odometry dataset.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• 한국통신학회논문지
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• 제11권1호
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• pp.1-15
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• 1986

Optic flow는 이동물체표면의 3차원 속도를 영상판(image plane)에 투영시킨 2차원 속도이다. 본 논문은 이 동물체의 연속영상으로부터 optic flow 를 구하는 기술들을 조사분석하고 정해진 optic flow로부터 물체의 인식 및 3차원 속도를 결정하는 기술들을 논하였다. 연속영상으로부터 구해진 optic flow는 영상압축기술인 영상간 부호(inter-frame image coding)에 해당되며 컴퓨터비젼 시스템(computer vision system)에서 이동물체 인식에 사용된다.

• Journal of Multimedia Information System
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• 제2권4호
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• pp.295-302
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• 2015

This article discusses the features and trends of development of the process of implementation of multimedia systems in various fields, research substantiate the basic concepts of multimedia systems, information flow, describes the classification and characterization of information flows and systems. Described container TIAV, which is designed with all the modern features and is aimed at future trends in the field of play.

• 전기학회논문지
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• 제64권7호
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2018년도 춘계 종합학술대회 논문집
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• pp.53-54
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• 2018

This paper introduces an approach to detect motion areas of stationary objects when the camera slightly moves in the scene by computing optical flow. The flow field is computed by two pyramidal architectures of 5 levels which are built by down-sampling the size of the images by half at each level. Two pyramids of images are built and then optical flow is computed at each level. A warping process combines the information and generates a final flow field after applying edge smoothness and outliers reduction steps. Moreover, we convert the flow vectors in order of magnitude and angle to a color map using a pseudo-color palette. Experimental results in the Middlebury optical flow dataset demonstrate the effectiveness of our method compared to other approaches.