Online recognition and segmentation for time-series motion with HMM and conceptual relation of actions
Online recognition and segmentation for time-series motion with HMM and conceptual relation of actions
复制标题
DOI：
10.1109/iros.2005.1545363
复制
发表时间：
2005-12
期刊：
影响因子：
--
通讯作者：
Taketoshi Mori;Yu Nejigane;M. Shimosaka;Y. Segawa;T. Harada;Tomomasa Sato
中科院分区：
文献类型：
--
作者：
Taketoshi Mori;Yu Nejigane;M. Shimosaka;Y. Segawa;T. Harada;Tomomasa Sato
在本文中，我们提出了一种鲁棒的在线动作识别算法，其分段方案可以检测动作发生的起点和终点。换句话说，该算法可靠地估计当前发生的动作类型。该算法具有以下特点： 1）算法结合了人类关于动作名称之间关系的知识，以简化和强化算法，因此我们的算法可以同时鲁棒地标记多个动作名称。 2）该算法使用时间序列动作概率，表示每个帧时间每个动作发生的可能性。 3）隐马尔可夫模型（HMM）的分类技术使算法能够鲁棒且立即检测分段点。使用真实动作捕捉数据的实验结果表明，我们的算法不仅有效降低了检测分段点的延迟，而且防止了系统由于时间序列动作概率的误差而做出不必要的分段。
In this paper, we propose a robust online action recognition algorithm with a segmentation scheme that detects start and end points of action occurrences. In other words, the algorithm estimates reliably what kind of actions occurring at present time. The algorithm has following characteristics: 1) The algorithm incorporates human knowledge about relation between action names in order to simplify and toughen the algorithm, thus our algorithm can label robustly multiple action names at the same time. 2) The algorithm uses time-series action probability that represents the likelihood of each action occurrence at every frame time. 3) The classification technique with hidden Markov models (HMMs) enables the algorithm to detect robustly and immediately the segmental points. The experimental results using real motion capture data show that our algorithm not only decreases effectively the latency for detecting the segmental points but also prevents the system from making unnecessary segments due to the error of time-series action probability.