• Korean Institute of Interior Design Journal
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• v.23 no.1
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• pp.122-131
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• 2014

This study aimed to conduct an eye-tracking experiment carried with facade images as a medium and to do research on 'the interest of the eyes' resulted from people's gazing phenomena. This study secured gazing data which appeared according to visual response and analyzed gazing phenomena to find the basic theory of 'the interest of the eyes' as a methodological role, which consumer interest and attention could be grafted when a plan and a design for space design was made. Data terms used in eye-tracking backgrounds and the movement of the eyes were investigated in literature review. Twenty (20) facade images were selected through a case study to get experimental stimuli for the related experiment. Thirty (30) subjects (men and women) suitable for the experiment were recruited to conduct an eye-tracking experiment. After the experiment, five (5) areas were set up in the facade image to identify the focus level of interest and attention. The level of interest and focus was connected to the interest of the eyes. The analysis to study the interest of the eyes was based on nine (9) items such as sequence, entry time, dwell time, hit ratio, revisits, revisitors, average fixation, first fixation and fixation count. Through gaze analysis, the following conclusion was drawn about the 'interest level of sight' for gaze frequency. The interest level can be interpreted to be higher for faster sequence, shorter entry time, longer all fixation(ms) for dwell time, faster all saccade(%), higher hit ratio, more revisits, more revisitors, longer average fixation, faster and longer first fixation, and more fixation count, and the person can be said to have felt interest faster and/or more.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.317-320
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• 2003

In this paper, we present real-time, accurate face region detection and tracking technique for an intelligent surveillance system. It is very important to obtain the high-resolution images, which enables accurate identification of an object-of-interest. Conventional surveillance or security systems, however, usually provide poor image quality because they use one or more fixed cameras and keep recording scenes without any clue. We implemented a real-time surveillance system that tracks a moving person using pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction. Color information in the ROI is updated to extract features for optimal tracking and zooming. The experiment with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.225-231
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• 2014

This paper suggests an algorithm to improve the tracking performance for an underwater acoustic target using the feedback information of acoustic feature of a target. While conventional tracking algorithms use detected acoustic signals only, the proposed algorithm uses detected acoustic signals and target feature information as well. Since the proposed algorithm tracks only the selected measurements using target feature information, it prevents onset of unnecessary tracks and improves tracking performance for target of interest. Furthermore, it optimizes tracking parameters for the target of interest and guarantees robustness and consistency of the track. Some simulations are performed to demonstrate the improved tracking performance of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.673-676
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• 2003

This paper deals with the problem of moving object detection and location in computer vision. We describe a new object-dependent motion analysis method for tracking target in an image sequence taken from a moving platform. We tackle these tasks with three steps. First, we make an active contour model of a target in order to build some of low-energy points, which are called kernels. Then we detect interest points in two windows called tracking windows around a kernel respectively. At the third step, we decide the correspondence of those detected interest points between tracking windows by the probabilistic relaxation method In this algorithm, the detecting process is iterative and begins with the detection of all potential correspondence pair in consecutive image. Each pair of corresponding points is then iteratively recomputed to get a globally optimum set of pairwise correspondences.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.628-631
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• 2003

In this paper, we present real-time, accurate face region detection and tracking technique for an intelligent surveillance system. It is very important to obtain the high-resolution images, which enables accurate identification of an object-of-interest. Conventional surveillance or security systems, however, usually provide poor image quality because they use one or more fixed cameras and keep recording scenes without any cine. We implemented a real-time surveillance system that tracks a moving person using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction. Color information in the ROI is updated to extract features for optimal tracking and zooming. The experiment with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.386-393
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• 2012

In this paper, we propose an efficient multi-object recognition and tracking scheme based on interest points of objects and their feature descriptors. To do that, we first define a set of object types of interest and collect their sample images. For sample images, we detect interest points and construct their feature descriptors using SURF. Next, we perform a statistical analysis of the local features to select representative points among them. Intuitively, the representative points of an object are the interest points that best characterize the object. in addition, we make the movement vectors of the interest points based on matching between their SURF descriptors and track the object using these vectors. Since our scheme treats all the objects independently, it can recognize and track multiple objects simultaneously. Through the experiments, we show that our proposed scheme can achieve reasonable performance.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.313-322
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• 2014

This paper is to survey and put in perspective the working methods of multi-target tracking in clutter. This paper includes theories and practices for data association and related filter structures and is motivated by increasing interest in the area of target tracking, security, surveillance, and multi-sensor data fusion. It is hoped that it will be useful in view of taking into consideration a full understanding of existing techniques before using them in practice.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.9-14
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• 2017

Embedded devices with the development of modern computer technology also began equipped with a variety of functions. In this study, to provide a method of tracking efficient face with a small instrument of resources, such as built-in equipment that uses an image sensor in recent years has been actively carried out. It uses a face detection method using the features of the MB-LBP in order to obtain an accurate face, specify the region (Region of Interest) around the face when the face detection for the face object tracking in the next video did. And in the video can not be detected faces, to track objects using the CAM-Shift key is a conventional object tracking method, which make it possible to retain the information without loss of object information. In this study, through the comparison with the previous studies, it was confirmed the precision and high-speed performance of the object tracking system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.118-121
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• 2003

In the conventional boundary estimation in EIT (Electrical Impedance Tomography), the interface between anomalies and background is expressed in usual as Fourier series and the boundary is reconstructed by obtaining the Fourier coefficients. This paper proposes a method for the boundary estimation, where the boundary of anomaly is approximated as the interpolation of front points located discretely along the boundary and is imaged by tracking the points in the region of interest. In the solution to the inverse problem to estimate the front points, the multi-layer neural network is introduced. For the verification of the proposed method, numerical experiments are conducted and the results indicate a good performance.