• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.489-492
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• 2016

Transportation in the logistics, many business organizations are engaged in monitoring and tracking the vehicles in order to improve logistics services, reduce expenses and secure security in cargo transportation. It is saving time and money by tracking and monitoring vehicles which transport cargo in supply chain of logistics. Therefore the main issue of delivery flow is to improve services, and ensure the safety in transportation system. This article suggests the tracking and monitoring model to keep safety transports on ICT network. It focuses on precise delivery control by monitoring and tracking vehicles to save time and costs. The status of product movement is analyzed for proper decision making. The vehicle embedded with RFID is automatically tracked in the movement process by tracking and monitoring model. The main role keeps safety tracking to reduce costs and to deliver products at proper time and location.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.329-334
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• 1998

This work is concerned with the problem of tracking a maneuvering target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly maneuvering tar-get. Many researches have been performed in tracking a maneuvering target. The conventional Interacting Multiple Model (IMM) filter is well known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation scheme. The subfilters of IMM can be considered as fusing its initial value with new measurements. This approach is also shown in this paper. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target as well as it reduces maximum values of estimation errors when maneuvering starts and finishes. The effectiveness of the pro-posed method is validated through simulation by comparing it with the conventional IMM algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3104-3120
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• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

• Spatial Information Research
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• 제21권3호
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• pp.11-19
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• 2013

도로에서 발생되는 차량간 충돌사고, 교통 소통 상황, 보행자 사고 등 다양한 도로 상황을 모니터링 및 자동으로 인식하여 교통정보를 제공하거나 긴급구난 서비스를 제공하기 위한 다양한 기술이 개발되고 있다. 도로 모니터링을 통한 다양한 객체 추적 및 상황인식을 위해서는 잡음 및 겹침 등에 강인한 객체 추적 기술이 요구된다. 본 논문에서는 외부 환경에서 Background Subtraction, LK-Optical Flow, 지역 기반 히스토그램 특징의 결합을 통해 추적을 위한 몇 가지 추정 인자를 생성하고 이를 통해 변화가 있는 객체, 잡음에도 비교적 강인한 추적 방법을 제안한다. 구체적으로는 객체의 초기 움직임 정보를 검출하기 위해 옵티컬 플로우를 적용하여 컬러 정보 및 밝기 변화에 무관한 이동 정보를 측정한다. 측정된 정보를 기반으로 하여 지역 히스토그램 기반 검증을 통해 신뢰도를 판단한다. 신뢰도가 낮을 경우 배경 제거 정보와 지역 히스토그램 트래커의 정보를 혼합하여 새로운 위치를 추정한다. 실험을 통해 제안된 기법이 객체를 추적하고 있는 도중 나타날 수 있는 충돌, 새로운 특징의 등장, 크기 변화 상황에 강인하게 동작함을 제시한다.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1299-1306
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• 2017

This paper presents a boundary tracking control of an agent. To this end, it is shown that the boundary tracking problem can be reformulated into a robust control of uncertain double integrator first. Then, a disturbance observer (DOB) based control is proposed solving the robust control problem. Unlike the existing results in the literature, the proposed DOB based control requires only the local position measurement of the boundary (not the gradient information). The performance of the proposed control is demonstrated for two cases: the measurement of the boundary is given in a continuous or discrete manner. Finally, it is shown that the proposed control can be used for environmental monitoring as well by showing that the agent follows a level curve of real environmental monitoring data.

• 한국위성정보통신학회논문지
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• 제7권1호
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• pp.134-138
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• 2012

본 논문에서는 자동화 생산 시설물의 객체모니터링을 위해서 생산라인 현장의 상태 감시 모니터링이 가능한 시스템을 구현하고자하며, 효율성을 향상시키기 위해 영상추적필터를 이용한 CCTV 영상추적 기술을 제안하고자 한다. 이 시스템에서는 자동화 생산 시설물의 객체모니터링을 위해 기존에 일반적으로 사용되었던 영상모니터링 방식이 아닌 영상추적필터를 기반으로 소프트웨어를 구축하여 효율적이며, 신뢰성 있는 데이터 전달을 함으로써 PC 기반의 모니터링이 가능하게 한다. 그리고 실시간 상태 확인이 가능하도록 함으로써 관리자의 접근성과 편의성을 향상시켰다. 또한 제안한 모니터링 시스템에 영상추적필터를 적용한 시뮬레이션을 수행함으로써 성능개선효과를 확인하였으며, 제안한 시스템의 효율성과 유용성을 확인하였다.

• 한국통신학회논문지
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• 제32권4C호
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• pp.401-410
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• 2007

본 논문에서는 비젼 카메라와 다중 객체 추적 방법을 이용한 실시간 수질 감시 시스템을 제안하였다. 제안된 시스템은 기존의 센서 방식의 감시 시스템과 달리 비젼 카메라를 이용해 객체를 개별적으로 분석한다. 비젼 카메라를 이용한 시스템은 영상에서 개별 객체를 분리해 내는 방법과, 연속하는 두 프레임간의 상관관계에 의해서 다수의 객체를 추적하는 방법으로 구성된다. 실시간 처리를 위해 비모수 예측을 사용하여 배경 영상을 생성하고 이를 이용해 객체를 추출한다. 비모수 예측을 이용하면 연산량을 줄이는 동시에 비교적 정확하게 객체를 추출 할 수 있다. 다중 객체 추적 방법은 개별 객체가 움직이는 방향, 속도 및 가속도를 이용해 다음 움직임을 예측하고 이를 기반으로 추적을 수행하였다. 또한 추적 성공률을 향상시키기 위해 예외처리 알고리즘을 적용하였다. 다양한 환경에서 실험한 결과 제안한 시스템은 처리 시간이 짧고 정확하게 다중 객체를 추적할 수 있어 실시간 수질 감시 시스템에 사용이 가능함을 확인하였다.

• Smart Structures and Systems
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• 제24권2호
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• 대한원격탐사학회지
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• 제24권3호
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• pp.235-243
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• 2008

Object tracking is a common vision task to detect and trace objects between consecutive frames. It is also important for a variety of applications such as surveillance, video based traffic monitoring system, and so on. An efficient moving vehicle clustering and tracking algorithm suitable for traffic monitoring system is proposed in this paper. First, automatic background extraction method is used to get a reliable background as a reference. The moving blob(object) is then separated from the background by mean shift method. Second, the scale invariant feature based method extracts the salient features from the clustered foreground blob. It is robust to change the illumination, scale, and affine shape. The simulation results on various road situations demonstrate good performance achieved by proposed method.

• 전기학회논문지
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• 제61권4호
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• pp.622-628
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• 2012

This paper proposes an environment monitoring algorithm using a behavior-based multiple robot system. This paper handles an escort and a boundary-tracking especially. Unlike previous research works, the proposed environment monitoring system which is based on the behavior-based multiple robot control allows the system to employ the reusable code and general algorithm. Also, the proposed method can be applied to cheaper process with low performances. In the proposed method, escort and boundary-tracking missions are constructed by weighted sum of predefined basic behaviors after redefining the basic behaviors in previous works and introducing the novel basic behavior. Simulation results of the proposed method are included to demonstrate the practical application of the proposed algorithm.