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中文摘要：
人体行为识别中的一个关键问题是如何表示高维的人体动作和构建精确稳定的人体分类模型．文中提出有效的基于混合特征的人体行为识别算法．该算法融合基于外观结构的人体重要关节点极坐标特征和基于光流的运动特征，可更有效获取视频序列中的运动信息，提高识别即时性．同时提出基于帧的选择性集成旋转森林分类模型（SERF），有效地将选择性集成策略融入到旋转森林基分类器的选择中，从而增加基分类器之间的差异性．实验表明SERF模型具有较高的分类精度和较强的鲁棒性．
英文摘要：
The representation of high dimensional human actions and the construction of accurate and stable human classification model are key issues in human action recognition. An efficient action recognition algorithm based on mixed features is proposed. Key joints of human body polar coordinates features basedon appearance structure and motion features based on optical flow are fused into the proposed algorithm to capture motion information in video sequences and improve the recognition instantaneity. Meanwhile, the selective ensemble rotation forest model （SERF） based on frame is developed and the selection ensemble strategy is used to select the base classifier of rotation forest and increase differences among the classifiers. Experimental results show the better classification accuracy and robustness of the proposed model.
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