• International journal of advanced smart convergence
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• v.12 no.4
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• pp.147-153
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• 2023

In recent times, an absence of effective crowd management has led to numerous stampede incidents in crowded places. A crucial component for enhancing on-site crowd management effectiveness is the utilization of crowd counting technology. Current approaches to analyzing congested scenes have evolved beyond simple crowd counting, which outputs the number of people in the targeted image to a density map. This development aligns with the demands of real-life applications, as the same number of people can exhibit vastly different crowd distributions. Therefore, solely counting the number of crowds is no longer sufficient. CSRNet stands out as one representative method within this advanced category of approaches. In this paper, we propose a crowd counting network which is adaptive to the change in the density of people in the scene, addressing the performance degradation issue observed in the existing CSRNet(Congested Scene Recognition Network) when there are changes in density. To overcome the weakness of the CSRNet, we introduce a system that takes input from the image's information and adjusts the output of CSRNet based on the features extracted from the image. This aims to improve the algorithm's adaptability to changes in density, supplementing the shortcomings identified in the original CSRNet.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.918-919
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• 2023

딥러닝 기술이 발전함에 따라 모델의 복잡성 역시 증가하고 있다. 본 연구에서는 모델 경량화를 위해 Knowledge Distillation 기법을 Crowd Counting Model에 적용했다. M-SFANet을 Teacher 모델로, 파라미터수가 적은 MCNN 모델을 Student 모델로 채택해 Knowledge Distillation을 적용한 결과, 기존의 MCNN 모델보다 성능을 향상했다. 이는 정확도와 메모리 효율성 측면에서 많은 개선을 이루어 컴퓨팅 리소스가 부족한 기기에서도 본 모델을 실행할 수 있어 많은 활용이 가능할 것이다.

• Korean Security Journal
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• no.58
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• pp.9-34
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• 2019

The Korean Police Force is equipped with the dual responsibility to not only protect the constitutional right to protest, but also prevent potential disorder and misconduct might be caused by the abuse of such a right. To this end, the Korean national police employ the crowd counting methodology, termed 'Maximum Figure at Any One Time' with a view to dispatching the proportionate number of police officers to protest scenes for safety management. However, protest organizers rather take advantage of 'Cumulative Figure' methodology, the purpose of which being to publicize the wide recognition of success, noticeably by demonstrating that as many people as possible support for their cause or voice. Hence, different estimates generated by different methods have raised serious political issues in Korean society. Nevertheless, it is found out that there are only three existing academic studies in Korea regarding crowd counting methods, and they are mainly geared towards comparing the two methods, unfortunately without any attempt to analyze the foreign literature in details. Keeping the research gap in mind, the research conducts a comprehensive review of the foreign literature with relation to protest crowd counting methods. Derived from the review and analysis, the counting methods can be broadly categorized into the three models such as: 1) Grid/Density Model, 2) Moving Crowds Model, and 3) Electronic & Non-Image Model. In the end, the research provides brief explanations regarding specific research findings per each model, and further, suggests some policy implications for the development of more accurate crowd counting methodology at protests in Korea.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.17-20
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• 2021

본 연구는 인원 계수에 딥러닝 알고리즘을 적용한다. 인원 계수는 안전 관리 분야, 상업 분야에 적용될 수 있다. 예를 들어, 건물 내 화재 발생 시, 계수된 인원을 활용하여 인명 피해를 최소화할 수 있다. 다른 예로, 유동인구 데이터를 기반으로 상권을 분석하여 경제적 효율성을 극대화할 수 있다. 이처럼 인원 데이터의 중요성이 증가함에 따라 인원 계수 연구도 활발하다. 그 예로, 객체 탐지(Object Detection) 같은 딥러닝 기반 인원 계수, 센서 기반 인원 계수 등이 있다. 본 연구에선 딥러닝 알고리즘인 VGGNet을 사용하여 인원을 계수했다. 결과로 Mean Absolute Percentage Error(이하 MAPE)는 약 5.9%의 오차율을 보였다. 결과 확인 방법으로는 설명 가능한 인공지능(XAI) 알고리즘 중 하나인 Grad-CAM을 적용했다.

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.612-623
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• 2022

Estimation of the presence of people in real time is extremely useful for businesses in providing better services. Many companies and researchers have attempted various researches in order to count the number of floating population in a specific space. Recently, as part of smart cities and digital twins, commercialization of measuring floating populations using Wi-Fi signals has become active in the public and private sectors. In this paper we present a method of estimating the floating population based on MAC-address values collected from smartphones. By distinguishing Real MAC-address and Random MAC-address values, we compare the estimated number of smartphone devices and the actual number of people caught on CCTV screens to evaluate the accuracy of the proposed method. And it appeared to have a similar correlation between the two datas. As a result, we present a method of estimating the floating population based on analyzing Wi-Fi Probe Requests.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.24-34
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• 2021

Estimation of the presence of people in real time is extremely useful for businesses in providing better services. Many companies and researchers have attempted various researches in order to count the number of floating population in specific space. Recently, as part of smart cities and digital twins, commercialization of measuring floating populations using Wi-Fi signals has become active in the public and private sectors. This paper explains the floating population measuring system from the perspective of general consumers(non-experts) who uses current population data. Specifically, it presents a method of estimating the floating population based on MAC-address values collected from smartphones. By distinguishing Real MAC-address and Random MAC-address values, we compare the estimated number of smartphone devices and the actual number of people caught on CCTV screens to evaluate the accuracy of the proposed method. And it appeared to have a similar correlation between the two datas. As a result, we present a method of estimating the floating population based on analyzing Wi-Fi Probe Requests

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.205-211
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• 2022

Performing the rituals of Hajj and Umrah is an obligation of Allah Almighty to all Muslims from all over the world. Millions of Muslims visit the holy mosques in Makkah every year to perform Hajj and Umrah. One of the most important pillars in Performing Hajj/Umrah is Tawaf and Sa'ay. Tawaf finished by seven rounds around the holy house (Al-Kabaa) and Sa'ay is also seven runs between As-Safa and Al-Marwa. Counting/knowing the number of runs during Tawaf/Sa'ay is one of the difficulties that many pilgrims face. The pilgrim's confusing for counting (Tawaf/Sa'ay) rounds finished at a specific time leads pilgrims to stay more time in Mataff bowl or Masa'a run causing stampedes and more crowded as well as losing the desired time for prayers to get closer to Almighty Allah in this holy place. These issues can be solved using effective crowd management systems for Tawaf/Sa'ay pillars, which is the topic of this research paper. While smart devices and their applications are gaining popularity in helping pilgrims for performing Hajj/Umrah activities efficiently, little has been dedicated for solving these issues. We present an autonomous Mobile-based framework for guiding pilgrims during Tawaf/Sa'ay pillars with the aid of GPS for points tracking and rounds counting. This framework is specially designed to prevent and manage stampedes during Tawaf/Sa'ay pillars, by helping pilgrims automatically counting the rounds during Tawaf/Sa'ay with supported Supplications (in written/audio form with different languages) from the Quran and Sunna'a.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.26-29
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• 2013

In order to estimate people crowdedness in public area, the texture based method or motion based method can be used. In this paper we have proposed a mixed method. By designating the region of interest, we made the degree of crowdedness more accurate. The feature normalization also reduced the image distortion which results from difference of camera angle. The proposed system was optimized to real time embedded hardware system.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.111-112
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• 2023

학교, 대형 쇼핑몰, 공항 등과 같은 큰 실내 공간에서는 군중의 동선과 밀도를 파악하고 관리하는 것은 안전사고와 연관되어 있어 매우 중요하다. 와이파이 센싱은 기존에 존재하던 CCTV 카메라나 센서를 활용한 혼잡도 관리보다 효율적이고 정확한 방식으로 추정하는 데 도움이 되며, 설치 및 유지보수 측면에서도 효율적이다. 본 논문에서는 실내 환경에서 군중 수를 추정하기 위해 딥 러닝을 이용한 무선랜 신호 분석 기법을 제안한다. 송수신기가 같은 공간에 위치했던 기존 연구들과는 달리 본 논문에서는 송신기와 수신기가 서로 다른 공간에 배치된 환경에서도 무선랜 수신 신호를 통해 다른 공간의 군중 수를 정확히 예측할 수 있다는 것을 실험으로 검증하였다.