상세정보
학술대회
Simple Yet Effective Approach to Repetitive Behavior Classification based on Siamese Network
- 발행일
- 202208
- 출처
- International Conference on Pattern Recognition (ICPR) 2022, pp.2993-2999
- 협약과제
- 22HS1200, 영유아/아동의 발달장애 조기선별을 위한 행동·반응 심리인지 AI 기술 개발, 유장희
- 초록
- Conventional studies dealing with repetition detection have mainly focused on tasks of temporal localization or counting the number of repetitions in videos. However, direct discrimination between repetitive and non-repetitive behaviors in videos, called repetitive behavior classification (RBC), has attracted less attention despite its great potential and advantages of: 1) filling the demands in the fields such as classification of repetitive behaviors in children with autism spectrum disorder (ASD) and helping to alleviate manual and time-consuming diagnostic procedures, 2) directly learning representation of differences between repetition and non-repetition patterns along the temporal dimension, and 3) being an effective alternative to the existing repetition counting and temporal segmentation tasks that are struggling with insufficient data and laborious manual annotation effort. In this paper, to the best of our knowledge, we firstly cast the problem of the RBC using deep learning frameworks. For this, we propose a simple yet effective add-on network, SiRepNet, that exploits the Siamese network structure to learn the inherent properties of repetitive behaviors. We also composed the RBC dataset by re-purposing and re-organizing Kinetics and Countix datasets for training our method. To validate our ideas, we carried out extensive experiments on RBC datasets, which showed performance improvement over state-of-the-art video classification algorithms by simply attaching our scheme to them for the RBC task.
- KSP 제안 키워드
- Behavior classification, Children with autism spectrum disorder(ASD), Classification algorithm, Deep learning framework, Insufficient data, Learning representation, Manual annotation, Repetitive behavior, Siamese network, Temporal dimension, Temporal localization