Liu Chin-De, Chung Yi-Nung, Julia Chung Pau-Choo
Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu 310, Taiwan.
IEEE Trans Inf Technol Biomed. 2010 Sep;14(5):1236-46. doi: 10.1109/TITB.2010.2052061. Epub 2010 Jun 7.
This paper presents an interaction-embedded hidden Markov model (IE-HMM) framework for automatically detecting and classifying individual human behaviors and group interactions. The proposed framework comprises a switch control (SC) module, an individual duration HMM (IDHMM) module, and an interaction-coupled duration HMM (ICDHMM) module. By analyzing the relative distances between the various participants in each scene, and monitoring the duration for which these distances are maintained, the SC module assigns each participant to an individual behavior unit (comprising a single participant) or an interaction behavior unit (comprising two or more participants). The individual behavior units are passed to the IDHMM module, which classifies the corresponding human behavior in accordance with the pose, motion, and duration information using duration HMM (DHMM). Similarly, the interaction behavior units are dispatched to the ICDHMM module, where the corresponding interaction mode is classified using an integrated scheme comprising multiple coupled-duration HMM (CDHMM), in which each state has an embedded coupled HMM (CHMM). The validity of the IE-HMM framework is confirmed by analyzing the human actions and interactions observed in a nursing home environment. The results confirm that the atomic behavior unit concept embedded in the SC module enables the IE-HMM framework to recognize multiple concurrent actions and interactions within a single scene. Overall, it is shown that the proposed framework has a recognition performance of 100% when applied to the analysis of individual human actions and 95% when applied to that of group interactions.
本文提出了一种用于自动检测和分类个体人类行为及群体互动的交互嵌入隐马尔可夫模型（IE-HMM）框架。所提出的框架包括一个切换控制（SC）模块、一个个体持续时间隐马尔可夫模型（IDHMM）模块和一个交互耦合持续时间隐马尔可夫模型（ICDHMM）模块。通过分析每个场景中各参与者之间的相对距离，并监测这些距离保持的持续时间，SC模块将每个参与者分配到一个个体行为单元（由单个参与者组成）或一个交互行为单元（由两个或更多参与者组成）。个体行为单元被传递到IDHMM模块，该模块使用持续时间隐马尔可夫模型（DHMM）根据姿势、运动和持续时间信息对相应的人类行为进行分类。类似地，交互行为单元被发送到ICDHMM模块，在那里使用一种包含多个耦合持续时间隐马尔可夫模型（CDHMM）的集成方案对相应的交互模式进行分类，其中每个状态都有一个嵌入的耦合隐马尔可夫模型（CHMM）。通过分析在养老院环境中观察到的人类行为和互动，证实了IE-HMM框架的有效性。结果证实，SC模块中嵌入的原子行为单元概念使IE-HMM框架能够识别单个场景内的多个并发行为和互动。总体而言，结果表明，所提出的框架在应用于个体人类行为分析时识别性能为100%，在应用于群体互动分析时为95%。
