• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.199-202
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• 2003

An effective method is proposed for detecting, acquisition and tracking of a moving object using a disturbance map method in complex image sequences. A significant moving object is detected and tracked within the field of view by computing a modified disturbance map method between an Input image and a temporal average image. This method is very efficient in the serveillance application of digital CCTV and an automatic tracking camera. Experimental results using a real image sequence confirmed that the proposed method can effectively detect and track a significant moving object in complex image sequences.

• 전기학회논문지
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• 제67권4호
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• pp.578-585
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• 2018

In this paper, we propose a classification and theft detection system for human and livestock for various moving objects in a barn. To do this, first, we extract the moving objects using the GMM method. Second, the noise generated when extracting the moving object is removed, and the moving object is recognized through the labeling method. And we propose a method to classify human and livestock using model formation and color for the unique form of the detected moving object. In addition, we propose a method of tracking and overlapping the classified moving objects using Kalman filter. Through this overlap determination method, an event notifying a dangerous situation is generated and a theft determination system is constructed. Finally, we demonstrate the feasibility and applicability of the proposed system through several experiments.

• 한국멀티미디어학회논문지
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• 제12권12호
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• pp.1745-1754
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• 2009

PTZ (Pan-Tilt-Zoom) camera based surveillance systems are enlarging their field of application due to their wide observable area. We aimed to detect both static and moving objects in automated working space by using a PTZ camera. For object detection we used background difference method because of the high quality segmentation. However, the method has a problem called 'hole' that is caused by non-continuous surveillance of the PTZ camera and its own characteristics. Moreover, the occlusion which occurs when the moving object overlaps with the static object should be solved for robust object detection. In this paper, we suggest a region-based technique for updating background images thereby overcoming the hole and occlusion problem. Through experiments with real scenes, it was verified that meaningful static and/or moving objects were detected very well.

• 한국통신학회논문지
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• 제27권3A호
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• pp.221-230
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• 2002

본 논문에서는 마코프 랜덤 필드를 이용해 움직이는 객체를 분할하는 새로운 방법을 제안하였다. 제안된 방법은 신호 탐지 이론에 기반을 두고 있다. 즉, 영상에서의 모션의 존재 유무는 binary decision rule에 의해 결정되고 잘못된 결정은 마코프 랜덤 필드 모델에 의해 수정된다. 전체적인 분할 과정은 2단계로 나뉘어진다. 첫 단계는 '모션탐지' 단계이며, 두번째 단계는 '객체분할' 단계이다. '모션탐지' 단계에서는 optical flow에 의해 발생하는 속도 벡터들에 대하여 binary decision rule을 적용하여 모tus의 존재 유무를 결정하는 과정이다. '객체분할' 단계에서는 첫 단계에서 원치 않게 발생하는 잡음을 제거한다. 이때 마코프 랜덤 필드로 가정하고 베이스 규칙에 의해 잡음을 제거한다. 실험결과, 연속영상에서 움직이는 객체의 영역을 효율적으로 분할함을 확인할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제6권2호
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• pp.41-47
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• 2001

본 연구에서는 카메라로부터 입력되는 영상열에서 사람과 같은 유동적인 이동 물체를 신뢰성 있게 판별하는 방법을 제안한다. 실시간 처리가 요구되는 시스템으로 빠른 수행속도와 적은 계산망, 신뢰성 있는 동작을 위해 입력영상과 참고영상에서 차영상을 구하고, 차영상의 히스토그램을 분석하여 여러개의 임계치을 결정한 후, 이를 사용하여 이동물체 영역을 신뢰성 있게 분리하고, 효율적으로 패턴을 분류할 수 있는 신경망을 이용하여 분리된 영역을 판별한다. 제안된 방법은 실제 상황에서 얻은 다양한 영상을 적용하여 실험하였으며, 4개층의 신경망을 적용하여 이동물체 검출 결과를 제시한다.

• ETRI Journal
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• 제45권5호
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• pp.795-810
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• 2023

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.

• 제어로봇시스템학회논문지
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• 제19권1호
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• pp.65-71
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• 2013

In this work, new smart phone based moving target detection is proposed. In order to implement the task, methods of real time image transmission from network camera, motion detecting algorithm and its effective implementation are also addressed. The network camera transfers image data by MJPEG format which contains various information such as data and IP address, and the smart phone separates the image data received through a WiFi module. Later, the image data is converted to a Bitmap image format, and with the help of the embedded OpenCV library on a smart phone and algorithm, it was found that the moving object was identified effectively in terms of real time monitoring and detection.

• 한국멀티미디어학회논문지
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• 제19권4호
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• pp.681-688
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• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.

• 한국정보통신학회논문지
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• 제19권10호
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• pp.2380-2388
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• 2015

영상에서 움직임이 있는 객체 영역을 검출하기 위한 이동 객체 검출(moving object detection; MOD) 알고리듬을 EGML(effective Gaussian mixture learning) 기반 배경 차분 방법을 적용하여 하드웨어로 설계하였다. EGML 계산 일부의 근사화를 통해 하드웨어 복잡도를 줄였으며, 파이프라이닝 적용을 통해 동작속도를 개선하였다. Verilog-HDL을 이용하여 하드웨어를 설계하였으며, MATLAB/Simulink와 FPGA가 연동된 FPGA- in-the-loop 환경에서 하드웨어 동작을 검증하였다. 설계된 MOD 프로세서는 XC5VSX95T FPGA 디바이스에서 2,218 슬라이스로 구현되었으며, 102 MHz의 클록 주파수로 동작하여 102 MS/s의 처리율을 갖는 것으로 평가되었다. IEEE CDW-2012 데이터 세트의 12가지 영상에 대해 MOD 프로세서의 성능을 분석한 결과, 평균 recall 값은 0.7631, 평균 precision 값은 0.7778, 그리고 평균 F-measure 값은 0.7535로 각각 평가되었다.

• 반도체디스플레이기술학회지
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• 제17권2호
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• pp.67-70
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• 2018

Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various application systems such as surveillance, military, and augmented reality. This paper proposes and implements a novel and more robust object recognition and tracking system to localize and track multiple objects from input images, which estimates target state using the likelihoods obtained from multiple CNNs. As the experimental result, the proposed algorithm is effective to handle multi-modal target appearances and other exceptions.