• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.215-217
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• 2021

본 논문은 3D 공간에서 사용자를 추출한 뒤, 체적 정보 분석을 통한 3D 스켈레톤(skeleton) 분석 과정을 통해 정확도 높은 다수 사용자의 위치 추적 기술에 대해 연구하였다. 이를 위하여 YOLO(You Only Look Once)를 활용하여 실시간으로 객체를 검출(Real-Time Object Detection)한 뒤 Google의 Mediapipe를 활용해 스켈레톤 추출, 스켈레톤 정규화(normalization)를 통한 스켈레톤의 크기 및 상대적 비율 계산, RGB 영상 스케일링(Scaling) 후 주요 마디 인접 영역의 RGB 색상 정보를 추출하는 방법을 통해 정확도가 개선된 높은 성능의 다중 사용자 추적 기술을 연구하였다.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.