• 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.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.27-34
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• 2013

Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.

• Journal of Korea Game Society
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• v.3 no.2
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• pp.30-34
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• 2003

Image-based rendering is an approach to generate realistic images in real-time without modeling explicit 3D geometry, Especially, TIP(Tour Into the Picture) is preferred for its simplicity in constructing 3D background scene. However, TP has a limitation that a viewpoint cannot go far from the origin of the TIP for the lack of geometrical information. in this paper, we propose a method to interpolating the TIP images to generate smooth and realistic navigation. We construct multiple TIP models in a wide area of the virtual environment. Then we interpolate foreground objects and background object respectively to generate smooth navigation results.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.31-36
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• 2020

Most of the background subtraction algorithms show good performance in static scenes. In the case of dynamic scenes, they frequently cause false alarm to "temporal clutter", a repetitive motion within a certain area. In this paper, we propose a robust technique for the multiple interval pixel sampling (MIS) algorithm to handle highly dynamic scenes. An adaptive threshold scheme is used to suppress false alarms in low-confidence regions. We also utilize multiple background models in the foreground segmentation process to handle repetitive background movements. Experimental results revealed that our approach works well in handling various temporal clutters.

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

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.848-855
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• 2008

In this paper, we describe correspondence among multiple images taken by multiple cameras. The correspondence among multiple views is an interesting problem which often appears in the application like visual surveillance or gesture recognition system. We use the principal axis and the ground plane homography to estimate foot of human. The principal axis belongs to the subtracted silhouette-based region of human using subtraction of the predetermined multiple background models with current image which includes moving person. For the calculation of the ground plane homography, we use landmarks on the ground plane in 3D space. Thus the ground plane homography means the relation of two common points in different views. In the normal human being, the foot of human has an exactly same position in the 3D space and we represent it to the intersection in this paper. The intersection occurs when the principal axis in an image crosses to the transformed ground plane from other image. However the positions of the intersection are different depend on camera views. Therefore we construct the correspondence that means the relationship between the intersection in current image and the transformed intersection from other image by homography. Those correspondences should confirm within a short distance measuring in the top viewed plane. Thus, we track a person by these corresponding points on the ground plane. Experimental result shows the accuracy of the proposed algorithm has almost 90% of detecting person for tracking based on correspondence of intersections.

• Geophysics and Geophysical Exploration
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• v.8 no.3
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• pp.207-217
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• 2005

We present a new approximate formulation of the integral equation (IE) method for models with variable background conductivity. This method overcomes the standard limitation of the conventional If method related to the use of a horizontally layered background only. The new approximate IE method still employs the Green's functions for a horizontally layered 1-D model. However, the new method allows us to use an inhomogeneous background with the IE method. The method was carefully tested for modeling the EM field for complex structures with a known variable background conductivity. It can find wide application in modeling EM data for multiple geological models with some common geoelectrical features, like a known inhomogeneous overburden, or salt dome structures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.101-104
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• 1997

Providing information on corporate level decision making for multiple decision makers in a consistent way is essential in Decision Support Systems. However, since the decision makers have different background and knowledge, the models used by them are also different in representation models. This makes the decision makers require a lot of efforts for model integration in an integrated decision making. The purpose of this paper is to propose an integration mechanism for synthetic use of multi-abstraction level decision making models. The proposed integration mechanism consists of model interpretation phase, model transformation phase and model integration phase. Specifically, the model transformation phase is divided into model tightening mode which gather information to makes a model transformed into upper level model, and model relaxing mode which makes lower level model.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.147.1-147.1
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• 2012

We present empirical fiducial sequences for horizontal-branch (HB) stars in a set of bright Galactic globular clusters previously observed in SDSS (An et al. 2008). Mean loci of HB stars are derived on color-magnitude diagrams with multiple color indices (u - g, g - r, g - i, and g - z ) in order to identify foreground/background objects as well as cluster RR Lyrae variables. We compare our fiducial sequences to the model predictions from Yonsei-Yale isochrones and test the accuracy of the stellar evolution models.