• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.67-72
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• 2015

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.73-77
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• 2015

This paper proposes an object tracking system according to reliability assessment of learning in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information that has the best reliability of learning. The standard object information is used for evaluating and learning the object that is successful tracking in tracking module. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track the reliable objects with reliability assessment of learning for the use of mobile platform.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.339-346
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• 2018

An Industry Foundation Classes(IFC), which is a data schema developed focusing on architecture, is being expanded to civil engineering structures. However, it is difficult to create an information model based on extended IFC since the BIM software cannot provide support functions. To manage a railway track based on the extended IFC, this paper proposed a method to create an alignment-centered separated railway track model and convert it to an extended IFC-based information model. First, railway track elements have been classified into continuous and discontinuous structures. The continuous structures were created by an alignment-based software, and discontinuous structures were created as independent objects through linkage of the discretized alignment. Second, a classification system and extended IFC schema for railway track have been proposed. Finally, the semantic information was identified by using the property of classification code and user interface. The availability of the methods was verified by developing an extended IFC-based information model of the Osong railway site.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.293-300
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• 2018

We propose a moving object extraction and tracking method for improvement of animal identification and tracking technology. First, we propose a method of merging separated moving objects into a moving object by using FCM (Fuzzy C-Means) clustering algorithm to solve the problem of moving object loss caused by moving object extraction process. In addition, we propose a method of extracting data from a moving object and a method of counting moving objects to determine the number of clusters in order to satisfy the conditions for performing FCM clustering algorithm. Then, we propose a method to continuously track merged moving objects. In the proposed method, color histograms are extracted from feature information of each moving object, and the histograms are continuously accumulated so as not to react sensitively to noise or changes, and the average is obtained and stored. Thereafter, when a plurality of moving objects are overlapped and separated, the stored color histogram is compared with each other to correctly recognize each moving object. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Journal of radiological science and technology
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• v.44 no.3
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• pp.231-237
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• 2021

The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of hospital hygiene and infection control in hospital settings. To raise hygiene awareness among ultrasound technicians, we evaluated the hygiene status of an ultrasound room, in comparison with that of objects used in daily life. Using the swab method, the following surfaces were examined: eight surfaces in the ultrasound room including the ultrasound probes (convex, linear, sector, 3D), ultrasound track ball, ultrasound keyboard, ultrasound gel (sealed and in use) and pillow as well as four surfaces of everyday objects including subway handles, common computer keyboards, common computer mouse, and cell phones. The streak plate technique was used for inoculation into media, which was observed for the formation of bacterial colonies following incubation for 24 h. Six bacterial strains were detected from objects used in the ultrasound room, including methicillin-resistant Staphylococcus aureus. Four strains of bacteria were detected on surfaces of everyday objects. The equipment and accessories used in an ultrasound room can act as vehicles for infecting patients. Establishment of standardized hygiene protocols and periodic training of the staff are recommended to avoid cross-infection.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.100-103
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• 2010

In this paper we propose a method of detecting moving object in H.264/AVC bitstream by representing the $4{\times}4$ block partition units as nodes of graph. By constructing hierarchical graph by taking into account the relation between nodes and the spatial-temporal relations between graphs in frames, we are able to track small objects, distinguish two occluded objects, and identify objects that move and stop alternatively.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.558-570
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2126-2133
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• 2014

We present a scalable object tracking framework, which is capable of removing shadows and tracking the people. The framework consists of background subtraction, fuzzy based shadow removal and boundary tracking algorithm. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects, and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects and shadows are processed differently in order to supply an object-based selective update. Experimental results demonstrate that the proposed method is able to track the object boundaries under significant shadows with noise and background clutter.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Smart Media Journal
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• v.1 no.3
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• pp.48-55
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• 2012

In general, background subtraction based methods are used to detect the moving objects in visual tracking applications. In this paper we employed background subtraction based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection and we compare those in terms of detection performance and computational complexity. In the first approach we used single background and in the second approach we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped object. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short time fully occlusion and illumination changes, as well as it can operate in real time.