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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >A daily behavior enabled hidden Markov model for human behavior understanding
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A daily behavior enabled hidden Markov model for human behavior understanding

机译：日常行为启用了隐藏的马尔可夫模型，以了解人类行为

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This paper presents a Hierarchical Context Hidden Markov Model (HC-HMM) for behavior understanding from video streams in a nursing center. The proposed HC-HMM infers elderly behaviors through three contexts which are spatial, activities, and temporal context. By considering the hierarchical architecture, HC-HMM builds three modules composing the three components, reasoning in the primary and the secondary relationship. The spatial contexts are defined from the spatial structure, so that it is placed as the primary inference contexts. The temporal duration is associated to elderly activities, so activities are placed in the following of spatial contexts and the temporal duration is placed after activities. Between the spatial context reasoning and behavior reasoning of activities, a modified duration HMM is applied to extract activities. According to this design, human behaviors different in spatial contexts Would be distinguished in first module. The behaviors different in activities would be determined in second module. The third module is to recognize behaviors involving different temporal duration. By this design, an abnormal signaling process corresponding to different situations is also placed for application. The developed approach has been applied for understanding of elder behaviors in a nursing center. Results have indicated the promise of the approach which can accurately interpret 85% of the elderly behaviors. For abnormal detection, the approach was found to have 90% accuracy, with 0% false alarm. (c) 2007 Elsevier Ltd. All rights reserved.
机译：本文提出了一种用于从护理中心的视频流中了解行为的分层上下文隐藏马尔可夫模型（HC-HMM）。拟议的HC-HMM通过空间，活动和时间背景三个环境来推断老年人的行为。通过考虑分层体系结构，HC-HMM构建了由三个部分组成的三个模块，在主要和次要关系中进行推理。空间上下文是根据空间结构定义的，因此将其放置为主推断上下文。时间持续时间与老年人的活动有关，因此活动被放置在以下空间环境中，时间持续时间被放置在活动之后。在活动的空间上下文推理和行为推理之间，将经过修改的持续时间HMM应用于提取活动。根据该设计，将在第一模块中区分在空间上下文中不同的人类行为。活动中不同的行为将在第二个模块中确定。第三模块是识别涉及不同时间持续时间的行为。通过这种设计，还可以放置与不同情况相对应的异常信令过程。所开发的方法已用于了解护理中心中的老年人行为。结果表明该方法有望准确解释85％的老年人行为。对于异常检测，发现该方法具有90％的准确性，误报率为0％。 （c）2007 Elsevier Ltd.保留所有权利。

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