• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.960-964
• /
• 2004

During system resource improvement process that based on Object-Oriented technology could be affect to the continuous system performance if lack appropriate management and control objects mechanism. This paper proposes a methodology to support continuous system performance and its stability. The adoption is based on Java Container Framework and Collections Framework for object collection. Also includes Software Engineering, Object Migration and Multiple Class Loaders mechanism accommodate to construct Continuous Migration Container (CMC). CMC is a runtime environment provides interfaces for management and control to support upgrading object process. Upgrade object methodology of CMC can be divided into two phase are object equivalence checking and object migration process. Object equivalence checking include object behavior verification and functional conformance verification before object migration process. In addition, CMC use Multiple Class Loaders mechanism to support reload effected classes instead of state transfer in migration process while upgrading object. These operations are crucial for system stability and enhancement efficiency.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.2
• /
• pp.88-92
• /
• 2011

This paper looks at the development of the visual system of robots, and the development of image processing algorism to measure the size of an object and the distance from robot to an object for the visual system. Robots usually get the visual systems with a camera for measuring the size of an object and the distance to an object. The visual systems are accurately impossible the size and distance in case of that the locations of the systems is changed and the objects are not on the ground. Thus, in this paper, we developed robot's visual system to measure the size of an object and the distance to an object using two cameras and two-degree robot mechanism. And, we developed the image processing algorism to measure the size of an object and the distance from robot to an object for the visual system, and finally, carried out the characteristics test of the developed visual system. As a result, it is thought that the developed system could accurately measure the size of an object and the distance to an object.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.07a
• /
• pp.42-45
• /
• 2010

This paper proposes an intelligent video surveillance system for human object tracking. The proposed system integrates the object extraction, human object recognition, face detection, and camera control. First, the object in the video signals is extracted using the background subtraction. Then, the object region is examined whether it is human or not. For this recognition, the region-based shape descriptor, angular radial transform (ART) in MPEG-7, is used to learn and train the shapes of human bodies. When it is decided that the object is human or something to be investigated, the face region is detected. Finally, the face or object region is tracked in the video, and the pan/tilt/zoom (PTZ) controllable camera tracks the moving object with the motion information of the object. This paper performs the simulation with the real CCTV cameras and their communication protocol. According to the experiments, the proposed system is able to track the moving object(human) automatically not only in the image domain but also in the real 3-D space. The proposed system reduces the human supervisors and improves the surveillance efficiency with the computer vision techniques.

• Journal of Sensor Science and Technology
• /
• v.30 no.2
• /
• pp.76-81
• /
• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• International Journal of Precision Engineering and Manufacturing
• /
• v.4 no.6
• /
• pp.61-70
• /
• 2003

A multibody system dynamics analysis program is presented using one of the most useful programming methodologies, the object-oriented modeling, The object-oriented modeling defines a problem from the physical world as an abstract object. The object becomes encapsulated with the data and method, Analysis is performed using the object's interface, It is then possible for the user and the developer to modify and upgrade the program without having particular knowledge of the analysis program, The method presented in this paper has several advantages, Since the mechanical components of the multi-body system are converted into the class, the modification, exchange, distribution and reuse of classes are increased. It becomes easier to employ a new analysis method and interface with other S/W and H/W systems, Information can be communicated to each object through messaging. This makes the modeling of new classes easier using the inheritance, When developing a S/W for the computer simulation of a physical system, it is reasonable to use object-oriented modeling.

• The KIPS Transactions:PartD
• /
• v.8D no.4
• /
• pp.401-408
• /
• 2001

Object-oriented software development methods are used to develop object-oriented software systems. Object-oriented systems are believed to habe better modularity, reusability, maintainability, and extensibility than systems modeled in conventional methods. Current object-oriented software development methods, however, are modeled in terms of procedural, functional, and structural models. There models cause problems such as tight coupling among activities, and uncontrolled access to global artifacts. In this paper, were introduce OMOS(Object-oriented software development Method for Object-oriented software System), an object-oriented modeling of object-oriented software development methods. Artifacts and their related activities are modeled as classes and objects. Development lifecycles are modeled as interactions among the objects. By modeling the software development method in an object-oriented way, OMOS achieves better reusability, flexibility, extensibility, and maintainability.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.1
• /
• pp.35-41
• /
• 2012

This paper proposes a moving object detection algorithm for active camera system that can be applied to mobile robot and intelligent surveillance system. Most of moving object detection algorithms based on a stationary camera system. These algorithms used fixed surveillance system that does not consider the motion of the background or robot tracking system that track pre-learned object. Unlike the stationary camera system, the active camera system has a problem that is difficult to extract the moving object due to the error occurred by the movement of camera. In order to overcome this problem, the motion of the camera was compensated by using SURF and Pseudo Perspective model, and then the moving object is extracted efficiently using stochastic Label Cluster transport model. This method is possible to detect moving object because that minimizes effect of the background movement. Our approach proves robust and effective in terms of moving object detection in active camera system.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.989-995
• /
• 2015

Due to the recent increase of the importance and demand of security services, the importance of a surveillance monitor system that makes an automatic security system possible is increasing. As the market for surveillance monitor systems is growing, price competitiveness is becoming important. As a result of this trend, surveillance monitor systems based on an embedded system are widely used. In this paper, an object detection algorithm based on an embedded system for a surveillance monitor system is introduced. To apply the object detection algorithm to the embedded system, the most important issue is the efficient use of resources, such as memory and processors. Therefore, designing an appropriate algorithm considering the limit of resources is required. The proposed algorithm uses two background models; therefore, the embedded system is designed to have two independent processors. One processor checks the sub-background models for if there are any changes with high update frequency, and another processor makes the main background model, which is used for object detection. In this way, a background model will be made with images that have no objects to detect and improve the object detection performance. The object detection algorithm utilizes one-dimensional histogram distribution, which makes the detection faster. The proposed object detection algorithm works fast and accurately even in a low-priced embedded system.

• International Journal of Advanced Culture Technology
• /
• v.4 no.4
• /
• pp.81-86
• /
• 2016

In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.18 no.2
• /
• pp.16-22
• /
• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.