• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.770-775
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• 2023

Recently, as the amount of digital data increases exponentially, the importance of data processing systems is being emphasized. In this situation, the selection and construction of data processing systems are becoming more important. In this study, the performance of cloud computing (CC), edge computing (EC), and UAV-based intelligent edge computing (UEC) was compared as a way to solve this problem. The characteristics, strengths, and weaknesses of each method were analyzed. In particular, this study focused on real-time large-capacity data processing situations such as corridor monitoring. When conducting the experiment, a specific scenario was assumed and a penalty was given to the infrastructure. In this way, it was possible to evaluate performance in real situations more accurately. In addition, the effectiveness and limitations of each computing method were more clearly understood, and through this, the help was provided to enable more effective system selection.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.54-61
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• 2015

In recent years, there has been increasing use of automatic surveillance and monitoring systems based on vision sensors. Humans are often the most important target in the systems, but processing human images is difficult due to the small sizes and flexible motions. Particularly, occlusion among pedestrians in camera images brings practical problems. In this paper, we propose a novel method to separate image regions of occluded pedestrians. A camera equipped with a wide angle lens is attached to the ceiling of a building corridor for sensing pedestrians with a wide field of view. The output images of the camera are processed for the human detection, tracking, identification, distortion correction, and occlusion handling. We resolve the occlusion problem adaptively depending on the angles and positions of their heads. Experimental results showed that the proposed method is more efficient and accurate compared with existing methods.

• Journal of The Korea Institute of Healthcare Architecture
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• v.22 no.3
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• pp.27-34
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• 2016

Purpose: The purpose of this paper is to develop a predictive model for patient visibility in Korean ICUs (corridor or continental type). Methods: The measures of static visibility were used to quantify the patient visibility (upper third part of the patient bed) from the nurse station. The measure of space programme and area distribution (patient zone percentage, staff zone percentage and departmental gross square meter per patient bed) were calculated by using AutoCAD and MS Excel programs. Regression analysis was conducted for visibility as dependant variable with independent variables of patient area percentage, staff area percentage as well as departmental gross square meter per bed by using IBM SPSS. Results: (1) Average patient visibility and percentage of patient area in ICU shows a strong negative correlation ($r^2$=0.66), p=0.01. (2) Patient visibility in Korean ICU (corridor or continental type) can be calculated as below with the given conditions: Y= $-1.449(X)+124.3{\pm}6$, Y is the total visibility of the ICU (corridor or continental type) and X is the percentage of patient area in the unit. Conditions:1. Given that the unit has a mixed programme of open bed and closed patient rooms and 2. The unit have a minimum of 20% patient rooms. Implications: This study may contribute to the visibility analysis of existing and future ICU design (corridor or continental type) in Korea to achieve maximum patient visibility and reduced patient mortality.

• Journal of The Korea Institute of Healthcare Architecture
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• v.22 no.4
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• pp.37-45
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• 2016

Purpose: The purpose of this paper is to analyze a sample of single corridor, pod type and composite type of ICUs in terms of patient visibility, spatial configurations and nurse walking distance focused on Korean cases. Methods: The measures of static visibility were used to quantify the patient visibility (upper third part of the patient bed) from the nurse station. The measure of space programme and area distribution (patient zone percentage, staff zone percentage, circulation zone percentage, corridor length per bed, distance from nurse station (NS) to patient bed and departmental gross square meter (DGSM) per patient bed) were calculated by using AutoCAD and MS Excel programs. In the second step of analysis the values of space distribution were compared among the three type of ICUs as well as the nurse walking distance, DGSM per bed and gross factor. in the third step of analysis regression analysis was conducted for the possible correlation between visibility and space programme and area distribution factors as well as nurse walking distance factors. Results: (1) It was found that on average composite type unit offer highest value of patient visibility followed by pod type, while single corridor type unit offers the minimum value of patient visibility among the three plan typologies. (2) Average patient visibility and DGSM per bed shows a strong positive correlation ($r^2=0.75$) and p=0.026. (3) Average patient visibility and average distance from NS has a strong negative correlation ($r^2=0.78$), and P=0.02. (4) On average composite type unit offer the minimum walking distance from NS (7.22 meter) followed by pod type unit (8.35 meter) and single corridor (9.76 meter). (5) Maximum distance from NS was noted in single corridor (18.19 meter) followed by pod type unit (15.14 meter) and Composite type unit (11.1 meter). Implications: This study may contribute to the visibility analysis of existing and future ICU design in Korea to achieve maximum patient visibility and reduced nurse walking distance.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1491-1500
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• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.355-361
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• 2006

In large scale environments like airport, museum, large warehouse and department store, autonomous mobile robots will play an important role in security and surveillance tasks. Robotic security guards will give the surveyed information of large scale environments and communicate with human operator with that kind of data such as if there is an object or not and a window is open. Both for visualization of information and as human machine interface for remote control, a 3D model can give much more useful information than the typical 2D maps used in many robotic applications today. It is easier to understandable and makes user feel like being in a location of robot so that user could interact with robot more naturally in a remote circumstance and see structures such as windows and doors that cannot be seen in a 2D model. In this paper we present our simple and easy to use method to obtain a 3D textured model. For expression of reality, we need to integrate the 3D models and real scenes. Most of other cases of 3D modeling method consist of two data acquisition devices. One for getting a 3D model and another for obtaining realistic textures. In this case, the former device would be 2D laser range-finder and the latter device would be common camera. Our algorithm consists of building a measurement-based 2D metric map which is acquired by laser range-finder, texture acquisition/stitching and texture-mapping to corresponding 3D model. The algorithm is implemented with laser sensor for obtaining 2D/3D metric map and two cameras for gathering texture. Our geometric 3D model consists of planes that model the floor and walls. The geometry of the planes is extracted from the 2D metric map data. Textures for the floor and walls are generated from the images captured by two 1394 cameras which have wide Field of View angle. Image stitching and image cutting process is used to generate textured images for corresponding with a 3D model. The algorithm is applied to 2 cases which are corridor and space that has the four wall like room of building. The generated 3D map model of indoor environment is shown with VRML format and can be viewed in a web browser with a VRML plug-in. The proposed algorithm can be applied to 3D model-based remote surveillance system through WWW.