• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2100-2103
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• 2003

기존의 객체 추출 및 추적 기법은 외형 변화가 없는 객체를 대상으로 하거나 배경이 고정된 영상만을 고려하였다 본 논문에서는 영역의 색상과 움직임 정보, 그리고 인접한 영역의 상관 관계를 고려한 Markov Random Field (MRF) 모델을 제안한다. MRF 모델은 영상의 시간적 공간적 상관성을 기반으로 최적의 레이블 셋을 계산함으로써 보다 정확하게 객체를 추출 및 추적할 수 있다. 또한, 블록 기반 움직임 추출 알고리즘인 Diamond Search (DS)를 분할된 영역에 적용하여 빠르게 영역의 움직임과 전역 움직임을 추정하였다. 실험 결과 제안한 방법이 객체의 외형 변화와 카메라 움직임이 있는 동영상에서 빠른 속도로 정확하게 객체를 추출 및 추적하는 것을 확인하였다.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.172-174
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• 2009

This paper describes an implementation method for the 'People Counting System' which detects and tracks moving people using a fixed single camera. This system proposes the method of improving performances by compensating weakness of existing algorithm. For increasing effect of detection, this system uses Single Gaussian Background Modeling which is more robust at noise and has adaptiveness. It minimizes unnecessarily detected area that is a limitation of the detecting method by using the background differences. And this system prevents additional detecting problems by removing shadow. Also, This system solves the problems of segmentation and union of people by using a new method. This method can work appropriately, if the angle of camera would not strictly vertical or the direction of shadow were lopsided. Also, by using integration System, it can solve a number of special cases as many as possible. For example, if the system fails to tracking, it will detect the object again and will make it possible to count moving people.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.472-477
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• 2016

The most important and critical to the performance of video surveillance systems is to be detected exactly how much. In order to accurately track the object must be able to accurately separate the background and object. However, the system itself rather than the human vision exactly distinguish the object and the background, to assess the situation, it is not easy. If we can accurately detect the background and the object, to be able to accurately track an object, it is possible to increase the reliability of the system, have a significant impact on the success of the entire production system. In this paper, we propose a way to distinguish more precisely the background and the object being to determine the background environment changes more accurately.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.721-728
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• 2005

This paper suggests effective object detection system for moving objects with specified color and motion information. The proposed detection system includes the object extraction and definition process which uses MCN(Multiple Color Normalization) and MCWUPC(Moving Color Weighted Unmatched Pixel Count) computation to decide the existence of moving object and object segmentation technique using signature information is used to exactly extract the objects with high probability. Finally, real time detection system is implemented to verify the effectiveness of the technique and experiments show that the success rate of object tracking is more than $89\%$ of total 120 image frames.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.209-215
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• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.423-430
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• 2024

The task of vision safety monitoring in construction environments presents a formidable challenge, owing to the dynamic and heterogeneous nature of these settings. Despite the advancements in artificial intelligence, the nuanced analysis of small or tiny personal protective equipment (PPE) remains a complex endeavor. In response to this challenge, this paper introduces an innovative safety monitoring system, specifically designed to enhance the safety monitoring of working both at ground level and at elevated heights. This novel system integrates a suite of sophisticated technologies: instance segmentation, shape classification, object tracking, a visualization report, and a real-time notification module. Collectively, these components coalesce to deliver a safety monitoring solution, ensuring a higher standard of protection for construction workers. The experimental results…..

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.1-10
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• 2019

Object-based image matching algorithms have been widely used in the image processing and computer vision fields. A variety of applications based on image matching algorithms have been recently developed for object recognition, 3D modeling, video tracking, and biomedical informatics. One prominent example of image matching features is the Scale Invariant Feature Transform (SIFT) scheme. However many applications using the SIFT algorithm have implemented based on stand-alone basis, not client-server architecture. In this paper, We initially implemented based on client-server structure by using SIFT algorithms to identify and match objects in biomedical images to provide useful information to the user based on the recently released Mobile platform. The major methodological contribution of this work is leveraging the convenient user interface and ubiquitous Internet connection on Mobile device for interactive delineation, segmentation, representation, matching and retrieval of biomedical images. With these technologies, our paper showcased examples of performing reliable image matching from different views of an object in the applications of semantic image search for biomedical informatics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.407-410
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• 2009

본 논문에서는 개선된 mean shift를 이용한 컬러 영상 분할을 소개한다. Mean shift는 Yizong Cheng에 의해 재조명되고 Dorin Comaniciu 등에 의해 정리되어 영상 필터링(image filtering), 영상 분할(image segmentation), 물체 추적(object tracking) 등 여러 응용 분야에 널리 활용되고 있다. 커널을 이용해 밀도를 추정하고 밀도가 가장 높은 점으로 커널을 연속적으로 이동함으로써 지역적으로 주요한 위치로 데이터 값을 갱신시킨다. 그러나 영상에 포함된 모든 화소에 대해 mean shift를 수행해야하기 때문에 연산 시간이 많이 소요되는 단점이 있다. 본 논문에서는 mean shift 필터링 과정을 분석하고 참조수렴방법과 강제수렴방법을 이용해 소요 시간을 단축시켰다. 모든 점에 대해 mean shift를 수행하는 대신 특정 조건을 만족하는 픽셀은 이웃 픽셀의 수렴 값을 참조하고, mean shift 과정에 진동 또는 미미한 이동을 계속하는 픽셀은 강제 수렴을 실시하였다. 개선된 방법과 기존의 mean shift 방식을 적용하여 영상 필터링과 영상 분할에 적용한 실험에서 결과 영상에는 차이가 적고 기존의 방법에 비해 수행 시간이 24% 정도 소요됨을 확인하였다.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.15-22
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• 2002

Image segmentation methods partition an image into meaningful regions. For image composition and analysis, it is desirable for the partitioned regions to represent meaningful objects in terms of human perception and manipulation. Despite the recent progress in image understanding, however, most of the segmentation methods mainly employ low-level image features and it is still highly challenging to automatically segment an image based on high-level meaning suitable for human interpretation. The concept of HCI (Human Computer Interaction) can be applied to operator-assisted image segmentation in a manner that a human operator provides guidance to automatic image processing by interactively supplying critical information about object boundaries. Intelligent Scissors and Snakes have demonstrated the effectiveness of human-assisted segmentation [2] [1]. This paper presents a method for interactive image segmentation for more efficient and effective detection and tracking of object boundaries. The presented method is partly based on the concept of Intelligent Scissors, but employs the well-established Canny edge detector for stable edge detection. It also uses "sewing method" for including weak edges in object boundaries, and 5-direction search to promote more efficient and stable linking of neighboring edges than the previous methods.