안내 로봇을 향한 관람객의 행위 인식 기반 관심도 추정
Estimating Interest Levels based on Visitor Behavior Recognition Towards a Guide Robot
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.
Abstract
This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.
참고문헌 (11)
- 임수현, "남성들의 미용에 대한 관심과 화장품 구매행동 연구", 한국의상디자인학회지, 16(4), pp.79~98 (2014), 한국의상디자인학회
- 정주원, "고등학생의 환경관심과 주관적 규범이 친환경 제품 구매의도에 미치는 영향: 친환경 제품·서비스에 대한 태도의 매개효과 분석", 환경교육, 32(4), pp.475~487 (2019), 한국환경교육학회[DOI]
- Hyunjun Eun, "Learning to Discriminate Information for Online Action Detection", 2020 IEEE, 2020, 809~818[DOI]
- Junwen Chen, "GateHUB: Gated History Unit with Background Suppression for Online Action Detection", 2022 IEEE, 2022, ~[DOI]
- Vittorio Mazzia, "Action Transformer: A self-attention model for short-time pose-based human action recognition", Pattern Recognition, 124, pp.108487~ (2022), Elsevier BV[DOI]
- Roberta Vrskova, "Human Activity Classification Using the 3DCNN Architecture", Applied Sciences, 12(2), pp.931~ (2022), MDPI AG[DOI]
- Hanan Salam, "Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions", IEEE Access, 5, pp.705~721 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Basura Fernando, "Modeling video evolution for action recognition", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5378~5387 (2015), IEEE
- Z. Gao, "Multi-dimensional human action recognition model based on image set and group sparisty", Neurocomputing, 215, pp.138~149 (2016), Elsevier BV[DOI]
- "ultralytics/yolov5: v7.0 – YOLOv5 SOTA Realtime Instance Segmentation"[URL]
- Yifu Zhang, "FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking", International Journal of Computer Vision, 129(11), pp.3069~3087 (2021), Springer Science and Business Media LLC[DOI]