• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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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.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.501-506
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• 2001

According to the current increase of the usefulness of information by Internet and Communication network, several methods are proposed in which multimedia information may be efficiently managed and serviced. This paper proposes FBOM(Fuzzy-Based Object Manager) using $\alpha$-cut in Object manager for Fuzzy-Based Multimedia Post-Office Box construction. The proposed system utilizes object discrimination, fuzzy filtering, and class generation structure in order to manage object using Fuzzy filtering. To know how well the proposed system are able to work, this paper have tested against the methods with 1000 items of multimedia information, and our system are compared with Random-key method and FBOM method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.157-165
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• 2004

Real-time(RT) object-oriented(OO) distributed computing is a form of RT distributed computing realized with a distributed computer system structured in the form of an object network. Several approached proposed in recent years for extending the conventional object structuring scheme to suit RT applications, are briefly reviewed. Then the approach named the TMO(Time-triggered Message-triggered Object)structuring scheme was formulated with the goal of instigating a quantum productivity jump in the design of distributed time triggered simulation. The TMO scheme is intended to facilitate the pursuit of a new paradigm in designing distributed time triggered simulation which is to realize real-time computing with a common and general design style that does not alienate the main-stream computing industry and yet to allow system engineers to confidently produce certifiable distributed time triggered simulation for safety-critical applications. The TMO structuring scheme is a syntactically simple but semantically powerful extension of the conventional object structuring approached and as such, its support tools can be based on various well-established OO programming languages such as C++ and on ubiquitous commercial RT operating system kernels. The Scheme enables a great reduction of the designers efforts in guaranteeing timely service capabilities of application systems. Start after striking space key 2 times.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.495-502
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• 2003

This paper focuses on the implementation of an efficient tracking method of a moving object using optimal representative blocks by way of a pan-tilt camera. The key idea is derived from the fact that when the image size of a moving object is shrunk in an image frame according to the distance between the mobile robot camera and the object in motion, the tracking performance of a moving object can be improved by reducing the size of representative blocks according to the object image size. Motion estimations using Edge Detection (ED) and Block-Matching Algorithm (BMA) are regularly employed to track objects by vision sensors. However, these methods often neglect the real-time vision data since these schemes suffer from heavy computational load. In this paper, a representative block able to significantly reduce the amount of data to be computed, is defined and optimized by changing the size of representative blocks according to the size of the object in the image frame in order to improve tracking performance. The proposed algorithm is verified experimentally by using a two degree-of- freedom active camera mounted on a mobile robot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• Journal of Applied Reliability
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• v.16 no.1
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• pp.48-55
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• 2016

Purpose: The purpose of this study is to identify the object-oriented metrics which have strong impact on the reliability and fault-proneness of software products. The reliability and fault-proneness of software product is closely related to the design properties of class diagrams such as coupling between objects and depth of inheritance tree. Methods: This study has empirically validated the object-oriented metrics to determine which metrics are the best to predict fault-proneness. We have tested the metrics using logistic regressions and artificial neural networks. The results are then compared and validated by ROC curves. Results: The artificial neural network models show better results in sensitivity, specificity and correctness than logistic regression models. Among object-oriented metrics, several metrics can estimate the fault-proneness better. The metrics are CBO (coupling between objects), DIT (depth of inheritance), LCOM (lack of cohesive methods), RFC (response for class). In addition to the object-oriented metrics, LOC (lines of code) metric has also proven to be a good factor for determining fault-proneness of software products. Conclusion: In order to develop fault-free and reliable software products on time and within budget, assuring quality of initial phases of software development processes is crucial. Since object-oriented metrics can be measured in the early phases, it is important to make sure the key metrics of software design as good as possible.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.611-623
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• 2013

Point cloud data (i.e., LiDAR; Light Detection and Ranging) collected by Airborne Laser Scanner (ALS) system is one of the major sources for surface reconstruction including DEM generation, topographic mapping and object modeling. Recently, demand and requirement of the accurate and realistic Digital Building Model (DBM) increase for geospatial platforms and spatial data infrastructure. The main issues in the object modeling such as building and city modeling are efficiency of the methodology and quality of the final products. Efficiency and quality are associated with automation and accuracy, respectively. However, these two factors are often opposite each other. This paper aims to introduce correction scheme of incorrectly determined Model Key Points (MKPs) regardless of the segmentation method. Planimetric and height locations of the MKPs were refined by surface patch fitting based on the Least-Squares Solution (LESS). The proposed methods were applied to the synthetic and real LiDAR data. Finally, the results were analyzed by comparing adjusted MKPs with the true building model data.

• ETRI Journal
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• v.25 no.2
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• pp.121-132
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• 2003

To enhance the productivity of software development and accelerate time to market, software developers have recently paid more attention to a component-based development (CBD) approach due to the benefits of component reuse. Among CBD processes, the identification of reusable components is a key but difficult process. Currently, component identification depends mainly on the intuition and experience of domain experts. In addition, there are few systematic methods or tools for component identification that enable domain experts to identify reusable components. This paper presents a systematic method and its tool called a component identifier that identifies software components by using object-oriented domain information, namely, use case models, domain object models, and sequence diagrams. To illustrate our method, we use the component identifier to identify candidates of reusable components from the object-oriented domain models of a banking system. The component identifier enables domain experts to easily identify reusable components by assisting and automating identification processes in an earlier development phase.