상세정보
학술대회
Hierarchical Pose Classification for Infant Action Analysis and Mental Development Assessment
- 저자
- Jianxiong Zhou, Zhongyu Jiang, 유장희, Jenq-Neng Hwang
- 발행일
- 202106
- 출처
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021, pp.1340-1344
- 협약과제
- 21HS1200, 영유아/아동의 발달장애 조기선별을 위한 행동·반응 심리인지 AI 기술 개발, 유장희
- 초록
- Based on Alberta Infant Motor Scale (AIMS), a questionnaire that tracks an infant's motor function, an infant's mental development can be evaluated by recording poses a baby can achieve. Therefore, it is meaningful to propose a systematic image-based pose classifier to classify infant actions based on AIMS to provide early diagnosis of a potential developmental disorder such as Autism. This paper presents a hierarchical pose classifier, given a baby image frame that combines the benefits of 3D human pose estimation and scene context information. Due to privacy policies, we cannot collect enough real infant images/videos for experiments. Instead, we generate synthetic baby images with the help of the Skinned Multi-Infant Linear (SMIL) model. Images are first fed into a ResNet-50 for coarse-level pose classification. A stacked hourglass CNN and a hierarchical 3D pose estimation scheme are used for 2D/3D pose estimation. Finally, an innovative Hierarchical Infant Pose Classifier (HIPC) takes the estimated 3D keypoints and coarse-level pose classification confidence scores to give the fine-level baby pose classification results. Our experimental results show that our hierarchical pose classifier achieves accurate and stable performance on infant pose recognition.
- KSP 제안 키워드
- 3D human pose estimation, 3D pose estimation, Action Analysis, Context Information, Developmental disorder, Early diagnosis, Image-based, Mental development, Motor function, Motor scale, Pose classification