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RISS 인기검색어
- 검색키워드
- 주제어 : Face Video Dataset (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Construction of a Video Dataset for Face Tracking Benchmarking Using a Ground Truth Generation Tool
Do, Luu Ngoc,Yang, Hyung Jeong,Kim, Soo Hyung,Lee, Guee Sang,Na, In Seop,Kim, Sun Hee The Korea Contents Association 2014 International Journal of Contents Vol.10 No.1
In the current generation of smart mobile devices, object tracking is one of the most important research topics for computer vision. Because human face tracking can be widely used for many applications, collecting a dataset of face videos is necessary for evaluating the performance of a tracker and for comparing different approaches. Unfortunately, the well-known benchmark datasets of face videos are not sufficiently diverse. As a result, it is difficult to compare the accuracy between different tracking algorithms in various conditions, namely illumination, background complexity, and subject movement. In this paper, we propose a new dataset that includes 91 face video clips that were recorded in different conditions. We also provide a semi-automatic ground-truth generation tool that can easily be used to evaluate the performance of face tracking systems. This tool helps to maintain the consistency of the definitions for the ground-truth in each frame. The resulting video data set is used to evaluate well-known approaches and test their efficiency.
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Construction of a Video Dataset for Face Tracking Benchmarking Using a Ground Truth Generation Tool
Luu Ngoc Do,Hyung Jeong Yang,Soo Hyung Kim,Guee Sang Lee,In Seop Na,Sun Hee Kim 한국콘텐츠학회(IJOC) 2014 International Journal of Contents Vol.10 No.1
In the current generation of smart mobile devices, object tracking is one of the most important research topics for computer vision. Because human face tracking can be widely used for many applications, collecting a dataset of face videos is necessary for evaluating the performance of a tracker and for comparing different approaches. Unfortunately, the well-known benchmark datasets of face videos are not sufficiently diverse. As a result, it is difficult to compare the accuracy between different tracking algorithms in various conditions, namely illumination, background complexity, and subject movement. In this paper, we propose a new dataset that includes 91 face video clips that were recorded in different conditions. We also provide a semi-automatic ground-truth generation tool that can easily be used to evaluate the performance of face tracking systems. This tool helps to maintain the consistency of the definitions for the ground-truth in each frame. The resulting video data set is used to evaluate well-known approaches and test their efficiency.
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데이터셋 프레임 길이에 따른 집중도 예측 모델 성능 비교
김동욱,김수균,윤여찬 한국컴퓨터교육학회 2024 컴퓨터교육학회 논문지 Vol.27 No.1
코로나19 이후 온라인 심리상담 수요 증가로, 비대면 환경에서 내담자의 감정 이해와 집중 유지가 중요해졌다. 이전 연구는 눈 깜빡임, 하품, 멍 때림 등을 기반으로 한 300프레임 길이의 DAiSEE(Dataset for Affective States in E-Environments) 데이터셋으로 집중도 분석을 했으나, 긴 프레임의 행동을 포착하는 데 한계가 있었다. 본 연구에서는 새로운 2000프레임의 데이터셋을 구축하여 머신러닝으로 집중도를 분석하고, 기존의 300프레임의 DAiSEE 데이터셋과 성능을 비교한다. 비교 결과 프레임 길이가 긴 데이터셋으로 학습할 때의 성능이 더 우수한 것을 확인한다. 또한, 상담자가 내담자의 상태를 직관적으로 이해할 수 있는 사용자 인터페이스를 개발해 상담 효과를 극대화할 것으로 기대된다.
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