• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.35-40
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• 2021

People counting data is crucial for most business owners, since they can derive meaningful information about customers movement within their businesses. For example, owners of the supermarkets can increase or decrease the number of checkouts counters depending on number of occupants. Also, it has many applications in smart buildings, too. Where it can be used as a smart controller to control heating and cooling systems depending on a number of occupants in each room. There are advanced technologies like camera-based people counting system, which can give more accurate counting result. But they are expensive, hard to deploy and privacy invasive. In this paper, we propose a method and a hardware sensor for counting people passing through a passage or an entrance using IR Infrared sensors. Proposed sensor operates at low voltage, so low power consumption ensure long duration on batteries. Moreover, we propose a new method that distinguishes human body and other objects. Proposed method is inexpensive, easy to install and most importantly, it is real-time. The evaluation of our proposed method showed that when counting people passing one by one without overlapping, recall was 96% and when people carrying handbag like objects, the precision was 88%. Our proposed method outperforms IR Infrared based people counting systems in term of counting accuracy.

• Fire Science and Engineering
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• v.31 no.6
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• pp.53-59
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• 2017

The purpose of this study is to verify the suitability of the current standards by predicting the density of the occupant load density for discount stores. An internal data survey as well as an actual survey using a People Counting System (PCS) were employed to ascertain the number of occupants and 95% confidence interval of nationwide discount stores. According to the results of the actual survey, the time and days on which the maximum number of occupants were reached was from 16:00 to 18:00 and Christmas Eve and the weekend before New Year's Day, respectively. From the results of the maximum number of occupants, a regression equation was derived from the relationship between the internal data and the amount of sales, and this equation was verified in a previous study. Thus, the internal data of 50 discount stores were analyzed using this process. As a result, the 95% confidence interval was determined to be $2.7{\sim}2.9m^2/pers.$ and the error level was not large compared to the domestic and foreign standards. Therefore, this study proposes that a conservative estimate of the standard occupant load density for discount stores is $2.7m^2/pers.$

• Journal of Environmental Health Sciences
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• v.41 no.5
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• pp.299-304
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• 2015

Objectives: A people-counting system measures real-time occupancy through motion detection. Accurate people-counting can be used to calculate suitable ventilation demands. This study determined the optimum motion threshold for a people-counting system. Methods: In a closed room with two occupants moving constantly, different thresholds were tested for the accuracy of a people-counting system. The experiments were conducted at 150, 300, 450 and 600 lux. These levels of brightness included the illumination levels of most public indoor areas. The experiments were repeated with three types of clothing coloration. Results: Overall, a threshold of 16 provided the lowest mean error percentage for the people-counting system. Brightness and clothing color did not have a significant impact on the results. Conclusion: A people-counting system could be used with threshold of 16 for most indoor environments.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.1-9
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• 2022

Localization systems can be used with various circumstances like measuring population movement and rescue technology, even in security technology (like infiltration detection system). Vision sensors such as camera often used for localization is susceptible with light and temperature, and can cause invasion of privacy. In this paper, we used ultra-wideband radar technology (which is not limited by aforementioned problems) and machine learning techniques to measure the number and location of occupants in other indoor spaces behind the wall. We used four different algorithms and compared their results, including extremely randomized tree for four different situations; detect the number of occupants in a classroom, split the classroom into 28 locations and check the position of occupant, select one out of the 28 locations, divide it into 16 fine-grained locations, and check the position of occupant, and checking the positions of two occupants (existing in different locations). Overall, four algorithms showed good results and we verified that detecting the number and location of occupants are possible with high accuracy using machine learning. Also we have considered the possibility of service expansion using the oneM2M standard platform and expect to develop more service and products if this technology is used in various fields.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• Journal of KIBIM
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• v.13 no.2
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• pp.29-36
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• 2023

This study explores the potential of utilizing video-based data analysis and machine learning techniques to estimate the number of occupants within a building. The research methodology involves developing a sophisticated counting system capable of detecting and tracking individuals' entry and exit patterns. The proposed method demonstrates promising results in various scenarios; however, it also identifies the need for improvements in camera performance and external environmental conditions, such as lighting. The study emphasizes the significance of incorporating machine learning in architectural and urban planning applications, offering valuable insights for the field. In conclusion, the research calls for further investigation to address the limitations and enhance the system's accuracy, ultimately contributing to the development of a more robust and reliable solution for building occupancy estimation.

• Wind and Structures
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• v.14 no.5
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• pp.383-411
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• 2011

Roof is an integral part of building envelope. It protects occupants from environmental forces such as wind, rain, snow and others. Among those environmental forces, wind is a major factor that can cause structural roof damages. Roof due to wind actions can exhibit either flexible or rigid system responses. At present, a dynamic test procedure available is CSA A123.21-04 for the wind uplift resistance evaluation of flexible membrane-roofing systems and there is no dynamic test procedure available in North America for wind uplift resistance evaluation of rigid membrane-roofing system. In order to incorporate rigid membrane-roofing systems into the CSA A123.21-04 testing procedure, this paper presents the development of a load cycle. For this process, the present study compared the wind performance of rigid systems with the flexible systems. Analysis of the pressure time histories data using probability distribution function and power spectral density verified that these two roofs types exhibit different system responses under wind forces. Rain flow counting method was applied on the wind tunnel time histories data. Calculated wind load cycles were compared with the existing load cycle of CSA A123.21-04. With the input from the roof manufacturers and roofing associations, the developed load cycles had been generalized and extended to evaluate the ultimate wind uplift resistance capacity of rigid roofs. This new knowledge is integrated into the new edition of CSA A123.21-10 so that the standard can be used to evaluate wind uplift resistance capacity of membrane roofing systems.