[논문]혼잡 환경에서 강인한 딥러닝 기반 인간 추적 프레임워크

오경석; 김성현; 김진섭; 이승환

doi:10.7746/jkros.2021.16.4.336

[국내논문] 혼잡 환경에서 강인한 딥러닝 기반 인간 추적 프레임워크
A Robust Deep Learning based Human Tracking Framework in Crowded Environments 원문보기

로봇학회논문지 = The journal of Korea Robotics Society, v.16 no.4, 2021년, pp.336 - 344

오경석 (School of Electronic Engineering, Kumoh National Institute of Technology) , 김성현 (School of Electronic Engineering, Kumoh National Institute of Technology) , 김진섭 (School of Electronic Engineering, Kumoh National Institute of Technology) , 이승환 (School of Electronic Engineering, Kumoh National Institute of Technology)

Abstract ▼ AI-Helper

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

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[국내논문] 혼잡 환경에서 강인한 딥러닝 기반 인간 추적 프레임워크
A Robust Deep Learning based Human Tracking Framework in Crowded Environments 원문보기

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[국내논문] 혼잡 환경에서 강인한 딥러닝 기반 인간 추적 프레임워크 A Robust Deep Learning based Human Tracking Framework in Crowded Environments 원문보기

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[국내논문] 혼잡 환경에서 강인한 딥러닝 기반 인간 추적 프레임워크
A Robust Deep Learning based Human Tracking Framework in Crowded Environments 원문보기

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