Gu Junxia, Ding Xiaoqing, Wang Shengjin, Wu Youshou
Department of Electronic Engineering, Tsinghua University, Beijing, China.
IEEE Trans Syst Man Cybern B Cybern. 2010 Aug;40(4):1021-33. doi: 10.1109/TSMCB.2010.2043526. Epub 2010 Apr 12.
A common viewpoint-free framework that fuses pose recovery and classification for action and gait recognition is presented in this paper. First, a markerless pose recovery method is adopted to automatically capture the 3-D human joint and pose parameter sequences from volume data. Second, multiple configuration features (combination of joints) and movement features (position, orientation, and height of the body) are extracted from the recovered 3-D human joint and pose parameter sequences. A hidden Markov model (HMM) and an exemplar-based HMM are then used to model the movement features and configuration features, respectively. Finally, actions are classified by a hierarchical classifier that fuses the movement features and the configuration features, and persons are recognized from their gait sequences with the configuration features. The effectiveness of the proposed approach is demonstrated with experiments on the Institut National de Recherche en Informatique et Automatique Xmas Motion Acquisition Sequences data set.
本文提出了一种通用的无视角框架，该框架融合了姿态恢复与分类，用于动作和步态识别。首先，采用一种无标记姿态恢复方法，从体数据中自动捕获三维人体关节和姿态参数序列。其次，从恢复的三维人体关节和姿态参数序列中提取多个配置特征（关节组合）和运动特征（身体的位置、方向和高度）。然后，分别使用隐马尔可夫模型（HMM）和基于样本的HMM对运动特征和配置特征进行建模。最后，通过融合运动特征和配置特征的分层分类器对动作进行分类，并利用配置特征从步态序列中识别出人员。在法国国家信息与自动化研究所圣诞运动采集序列数据集上进行的实验证明了该方法的有效性。
