• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1169-1193
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• 2010

In this paper, we present and compare two different multiple camera collaboration strategies to reduce false association in finding the correspondence of objects. Collaboration matrices are defined with the required minimum separation for an effective collaboration because homographic lines for objects association are ineffective with the insufficient separation. The first strategy uses the collaboration matrices to select the best pair out of many cameras having the maximum separation to efficiently collaborate on the object association. The association information in selected cameras is propagated to unselected cameras by the global information constructed from the associated targets. While the first strategy requires the long operation time to achieve the high association rate due to the limited view by the best pair, it reduces the computational cost using homographic lines. The second strategy initiates the collaboration process of objects association for all the pairing cases of cameras regardless of the separation. In each collaboration process, only crossed targets by a transformed homographic line from the other collaborating camera generate homographic lines. While the repetitive association processes improve the association performance, the transformation processes of homographic lines increase exponentially. The proposed methods are evaluated with real video sequences and compared in terms of the computational cost and the association performance. The simulation results demonstrate that the proposed methods effectively reduce the false association rate as compared with basic pair-wise collaboration.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.3
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• pp.264-271
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• 2013

A method of multiple transport-aircraft mission modeling is proposed in order to improve the efficiency of evaluating and optimizing pre-mission plans. To deal with the challenge of multiple transport-aircraft missions, the object-oriented modeling method is utilized. The elements of the mission are decomposed into objects and businesses, And the major mission objects and their important properties are summarized. A complex mission can be broken down into basic business modules such as the ground section and flight section. The business models of loading and fueling services in the ground section are described. The business model of the flight section is composed of an air route and flight profile with the flight equation and the fuel consumption model. The logical relationship of objects and business modules is introduced. The architecture of the simulation system, which includes a database, computation module, graphical user interface (GUI) module, and a result analysis module, is established. A sample case that includes two different plans is provided to verify the model's ability to achieve multi-aircraft composite mission simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.358-379
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• 2010

Multiple object association is an important capability in visual surveillance system with multiple cameras. In this paper, we introduce locally initiating line-based object association with the parallel projection camera model, which can be applicable to the situation without the common (ground) plane. The parallel projection camera model supports the camera movement (i.e. panning, tilting and zooming) by using the simple table based compensation for non-ideal camera parameters. We propose the threshold distance based homographic line generation algorithm. This takes account of uncertain parameters such as transformation error, height uncertainty of objects and synchronization issue between cameras. Thus, the proposed algorithm associates multiple objects on demand in the surveillance system where the camera movement dynamically changes. We verify the proposed method with actual image frames. Finally, we discuss the strategy to improve the association performance by using the temporal and spatial redundancy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.1-9
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• 2007

In this paper, we propose a new 3D modeling and rendering system using multiple stereo cameras. When target objects are captured by cameras, each capturing PC segments the objects and estimates disparity fields, then they transmit the segmented masks, disparity fields, and color textures of objects to a 3D modeling server. The modeling server generates 3D models of the objects from the gathered masks and disparity fields. Finally, the server generates a video at the designated point of view with the 3D model and texture information from cameras.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.51-57
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• 2008

In recent computer vision or robotics fields, the research area of object recognition from image using low cost web camera or other video device is performed actively. As study for this, there are various methodologies suggested to retrieve objects in robotics and vision research areas. Also, robotics is designed and manufactured to aim at doing like human being. For instance, a person perceives apples as one see apples because of previously knowing the fact that it is apple in one's mind. Like this, robotics need to store the information of any object of what the robotics see. Therefore, in this paper, we propose an methodology that we can rapidly recognize objects which is stored in object database by using SIFT (scale invariant feature transform) algorithm to get information about the object. And then we implement the methodology to enable to recognize simultaneously multiple objects in an image.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.583-586
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• 2002

In this research, we propose a flexible conveying system (FCS) which consists of multiple arrays of cells. Each cell is a wheel driven by a two degree-of-freedom mechanism. The direction and velocity of cell are controlled based on the concept of hybrid control under a distributed network. Each cell has its own controller under a subsumption architecture for low-level control. A cell communicates with its four neighboring cells to manipulate n targeted object towards its desired position. The high-level control assigns a desired position and direction of the object to each cell. The path of each object is generated by many supporting cells. Moreover, the FCS can handle multiple objects simultaneously. To study the flexible conveying system, a GUI-based simulator of flexible conveying system is constructed. The simulated results show that the system can handle multiple objects independently and simultaneously under the proposed hybrid control architecture.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.719-732
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• 2006

A set of clustering algorithms with proper weight on the formulation of distance which extend to mixed numeric and multiple binary values is presented. A simple matching and Jaccard coefficients are used to measure similarity between objects for multiple binary attributes. Similarities are converted to dissimilarities between i th and j th objects. The performance of clustering algorithms with balancing weight on different similarity measures is demonstrated. Our experiments show that clustering algorithms with application of proper weight give competitive recovery level when a set of data with mixed numeric and multiple binary attributes is clustered.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.673-679
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• 2004

In this paper, we develop the displacement measurement system of multiple moving objects based on image processing techniques. The image processing method adopts inertia moment theory for obtaining the centroid measurement of the targets and basic processing algorithm of gray, binary, closing, labeling and so on. To get precise displacement measurement in spite of multiple moving targets, a CGD camera with zoom is used and the position of camera is changed by a pan/tilt system. The fiducial marks on the fixed positions are used as the sensing points for the image processing to recognize the position errors in direction of XY-coordinates. The precise alignment device is pan/tilt of XY-type and the pan/tilt is controlled by DC servomotors which are driven by a microprocessor. Morover, the centers of fiducial marks are obtainted by an inertia moment method. By applying the developed precise position control system for multiple targets, the displacement of multiple moving targets are detected automatically and are also stored in the database system in a real time. By using database system and internet, the displacement datum can be confirmed at a great distance and analyzed. Finally, the effectiveness of developed system is shown in experimental results and realized the precision about 0.12[mm] in the position control of XY-coordinates.