• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.320-324
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• 2023

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.362-371
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• 2013

This paper presents a framework of real-time augmented reality on 3-D mobile display with stereo camera tracking. In the framework, camera poses are jointly estimated with the geometric relationship between stereoscopic images, which is based on model-based tracking. With the estimated camera poses, the virtual contents are correctly augmented on stereoscopic images through image rectification. For real-time performance, stereo camera tracking and image rectification are efficiently performed using multiple threads. Image rectification and color conversion are accelerated with a GPU processing. The proposed framework is tested and demonstrated on a commercial smartphone, which is equipped with a stereoscopic camera and a parallax barrier 3-D display.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.1
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• pp.1-13
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• 2007

This study was carried out to evaluate the applicability of the odor source tracking using wind field and fingerprint as a solution tool. First of all, CALMET and HYSPLIT modeling system, and database of odor discharge companies were utilized to track odor from industrial complexes. Secondly, industrial odor fingerprint was made by listing on the 19 domestic industries, and compared with foreign data to assess the representative, and thus the similarity was 86.7%. On the modeling experiment, Sihwa industrial complex did not show any difference because the matching rates of day and night were 49.5% and 50.0%, respectively. However, the Banwol and Sihwa industrial complexes did show some differences due to odor facility density. Separately, in this study, odor samples were obtained from 10 odor discharging companies, located in the Sihwa and Banwol industrial complexes, They were compared with the results of odor tracking modeling. The matched companies were 4 of 10 by three cases of tracking, while the fingerprint and industry of odor monitoring networks and companies matched each other. Therefore, this study confirmed the approach applicability of source tracking system using the fingerprint.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.178-181
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• 2011

With the development of mobile devices and intelligent surveillance system loaded with pan/tilt cameras, object tracking with non-stationary cameras has become a topic with increasing importancy. Since it is difficult to model a background image in a non-stationary camera environment, colors and texture are the most important features in the tracking algorithm. However, colors in the background similar to those in the target arise instability in the tracking. Recently, we proposed a robust color based tracking algorithm that uses an area weighted centroid shift. In this letter, we update the model such that it becomes more stable against background colors. The proposed algorithm also incorporates time filtering by adding an additional energy term to the energy functional.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2094-2112
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• 2019

Focusing on the issue that the conventional Kernel Correlation Filter (KCF) algorithm has poor performance in handling scale change and obscured objects, this paper proposes an anti-occlusion and scale adaptive tracking algorithm in the basis of KCF. The average Peak-to Correlation Energy and the peak value of correlation filtering response are used as the confidence indexes to determine whether the target is obscured. In the case of non-occlusion, we modify the searching scheme of the KCF. Instead of searching for a target with a fixed sample size, we search for the target area with multiple scales and then resize it into the sample size to compare with the learnt model. The scale factor with the maximum filter response is the best target scaling and is updated as the optimal scale for the following tracking. Once occlusion is detected, the model updating and scale updating are stopped. Experiments have been conducted on the OTB benchmark video sequences for compassion with other state-of-the-art tracking methods. The results demonstrate the proposed method can effectively improve the tracking success rate and the accuracy in the cases of scale change and occlusion, and meanwhile ensure a real-time performance.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.11
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• pp.43-51
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• 2021

A robust tracking controller design was developed for a rotary motion control system. The friction force versus the angular velocity was measured and modeled as a combination of linear and nonlinear components. By adding a model-based friction compensator to a nominal proportional-integral-derivative controller, it was possible to build a simulated control system model that agreed well with the experimental results. A zero-phase error tracking controller was selected as the feedforward tracking controller and implemented based on the estimated closed-loop transfer function. To provide robustness against external disturbances and modeling uncertainties, a disturbance observer was added in the position feedback loop. The performance improvement of the overall tracking controller structure was verified through simulations and experiments.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.1-9
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• 2013

Accuracy of the 3-D model is essential in model-based camera tracking. However, 3-D object modeling requires dedicated and complicated procedures for precise modeling without any errors. Even if a 3-D model contains a certain level of errors, on the other hand, the tracking errors cause by the modeling errors can be different from its perceptual errors; thus, it is an important aspect that the camera tracking can be successful without precise 3-D modeling if the modeling errors are within the user's permissible range. In this paper, we analyze the tolerance of 3-D object modeling errors by comparing computational matching errors with perceptual matching errors through user evaluations, and also discuss permissible ranges of 3-D object modeling errors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2335-2340
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• 2009

This paper presents how to implement Markerless Augmented Reality and how to create and apply reference data sets. There are three parts related with implementation: setting camera, creation of reference data set, and tracking. To create effective reference data sets, we need a 3D model such as CAD model. It is also required to create reference data sets from various viewpoints. We extract the feature points from the mode1 image and then extract 3D positions corresponding to the feature points using ray tracking. These 2D/3D correspondence point sets constitute a reference data set of the model. Reference data sets are constructed for various viewpoints of the model. Fast tracking can be done using a reference data set the most frequently matched with feature points of the present frame and model data near the reference data set.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.323-336
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• 2021

This study investigated the cognitive process of fashion design images through eye activity tracking. Differences in the cognitive process and gaze activity according to image elements were confirmed. The results of the study are as follows. First, a difference was found between groups in the gaze time for each section according to the model and design. Although model diversity is an important factor leading the interest of observers, the simplicity of the model was deemed more effective for observing the design. Second, the examination of the differences by segments regarding the gaze weight of the image area showed differences for each group. When a similar type of model is repeated, the proportion of face recognition decreases, and the proportion of design recognition time increases. Conversely, when the model diversity is high, the same amount of time is devoted to recognizing the model's face in all the processes. Additionally, there was a difference in the gaze activity in recognizing the same design according to the type of model. These results enabled the confirmation of the importance of the model as an image recognition factor in fashion design. In the fashion industry, it is important to find a cognitive factor that attracts and retains consumers' attention. If the design recognition effect is further maximized by finding service points to be utilized, the brand's sustainability is expected to be enhanced even in the rapidly changing fashion industry.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1561-1564
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• 1997

In the study, simulation result was studied by connecting PID controller in series to the established Neural Networks Controller. Neural Network model is composed of two layers to evaluate tracking performance improvement. The reqular dynamic characteristics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance inprovement was developed more in case of connecting PID than Neural Network Contoller and that tracking plant parameter in 251 sample was approached rapidly case of time variable.