• 한국정보디스플레이학회:학술대회논문집
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• 2000.01a
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• pp.79-80
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• 2000

General stereo vision system shows things in 3D, using two visions of left and right side. When the viewpoints of left/right sides are not in accord with each other, it gives fatigue to human eyes and prevents them from having the 3-D feeling. Also, it would be difficult to track mobile objects that are not in the middle of a screen. Therefore, the object tracking function of stereo vision system is to control tracking objects to always be in the middle of a screen while controlling convergence angles of mobile objects in the input image of the left/right cameras. In this paper, object-tracker in stereo vision system is presented which would track mobile objects by using block matching algorithm of preprocessing and JTC.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.110-114
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• 2019

Recently, video monitoring system technology has been rapidly developed to monitor and respond quickly to various situations. In particular, computer vision and related research are being actively carried out to track objects in the video. This paper proposes an efficient multiple objects detection method based on convolutional neural network (CNN) for multiple objects tracking. The results of the experiment show that multiple objects can be detected and tracked in the video in the proposed method, and that our method is also good performance in complex environments.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2901-2919
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• 2012

Recent research in MEMS (Micro-Electro-Mechanical Systems) and wireless communication has enabled tracking of continuous objects, including fires, nuclear explosions and bio-chemical material diffusions. This paper proposes an energy-efficient scheme that detects and tracks different dynamic shapes of a continuous object (i.e., the inner and outer boundaries of a continuous object). EBCO (Efficient Boundary detection and tracking of Continuous Objects in WSNs) exploits the sensing capabilities of sensor nodes by automatically adjusting the sensing range to be either a boundary sensor node or not, instead of communicating to its neighboring sensor nodes because radio communication consumes more energy than adjusting the sensing range. The proposed scheme not only increases the tracking accuracy by choosing the bordering boundary sensor nodes on the phenomenon edge, but it also minimizes the power consumption by having little communication among sensor nodes. The simulation result shows that our proposed scheme minimizes the energy consumption and achieves more precise tracking results than existing approaches.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.43-53
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• 2012

In the this paper, a gesture-based user interface based on object detection using predefined gesture and the tracking of the detected object is proposed. For object detection, moving objects in a frame are computed by comparing multiple previous frames and predefined gesture is used to detect the target object among those moving objects. Any object with the predefined gesture can be used to control. We also propose an object tracking algorithm, namely density based meanshift algorithm, that uses color distribution of the target objects. The proposed object tracking algorithm tracks a target object crossing the background with a similar color more accurately than existing techniques. Experimental results show that the proposed object detection and tracking algorithms achieve higher detection capability with less computational complexity.

• Architectural research
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• v.22 no.4
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• pp.155-162
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• 2020

Numerous scholars have explored the modeling, control, and optimization of energy systems in buildings, offering new insights about technology and environments that can advance industry innovation. Eye trackers deliver objective eye-gaze data about visual and attentional processes. Due to its flexibility, accuracy, and efficiency in research, eye tracking has a control scheme that makes measuring rapid eye movement in three-dimensional space possible (e.g., virtual reality, augmented reality). Because eye movement is an effective modality for digital interaction with a virtual environment, tracking how users scan a visual field and fix on various digital objects can help designers optimize building environments and materials. Although several scholars have conducted Virtual Reality studies in three-dimensional space, scholars have not agreed on a consistent way to analyze eye tracking data. We conducted eye tracking experiments using objects in three-dimensional space to find an objective way to process quantitative visual data. By applying a 12 × 12 grid framework for eye tracking analysis, we investigated how people gazed at objects in a virtual space wearing a headmounted display. The findings provide an empirical base for a standardized protocol for analyzing eye tracking data in the context of virtual environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.894-897
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• 2012

We propose a effective method using the HOG (Histogram of Oriented Gradients) feature vector to track individual objects in an environment which multiple objects are moving. The proposed algorithm consists of pre-processing, object detection and object tracking. We experimented with six videos which have various trajectories and the movement. When occlusion between objects was occurred, we identified individual object by using center and predicted coordinates of moving objects. The algorithm shows 85.45% of tracking rate in the videos we experimented. We expect the proposed system is utilized in security systems which require the alalysis of the position and motion pattern of objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• 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 IEEK Conference
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• 2002.06d
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• pp.137-140
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• 2002

This paper presents a new variational framework for detecting and tracking moving objects in image sequence. Motion detection is performed using Level Set Model. The original frame is used to provide th moving object boundaries Then, the detection and the tracking problem are addressed in a common framework that employs a inward-outward curve evolution function. This function is minimized using a gradient decent method.