基于多特征融合与多级多模式分类的人体动作识别技术研究

项目名称： 基于多特征融合与多级多模式分类的人体动作识别技术研究

项目编号： No.U1204617

项目类型： 联合基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 王峰

作者单位： 河南工业大学

项目金额： 30万元

中文摘要： 视角变化、动作执行速度和人体结构差异等可变因素是造成人体动作识别困难的关键所在，本项目从动作描述、动作建模和动作识别三方面展开研究：（1）在动作描述方面，研究基于多特征融合的动作描述算法，从整体和局部、时间和空间等不同层次提取具有互补性的静态与动态动作特征，减少可变因素的影响，提高特征描述的鲁棒性；（2）在动作建模方面，研究基于MKL的非线性SVM动作模型学习算法，在此基础上研究基于迁移学习的动作模型学习算法，提高动作模型的可扩展性；（3）在动作识别方面，针对关键帧图像中的关键姿态，研究基于关键姿态模板匹配的动作粗分类算法；针对关键帧子视频中的时空特性，研究基于MKL的非线性SVM动作细分类算法；同时，针对动作识别中存在的不确定性，研究基于模糊逻辑的动作分类算法，从而通过多级多模式的分类方法提高动作识别算法的整体性能。项目的顺利开展将会为行为分析和理解等其他智能化的应用提供更有效的依据。

中文关键词： 动作识别；多特征融合；机器学习；多级分类；多模式分类

英文摘要： The major challenge of human action recognition is to deal with all kinds of variabilities, such as different viewpoints, different acting speeds and styles, different genders and sizes. To address these concerns, the research is carried out from three aspects as action description, action modeling and action recognition. (1) For action description, action description algorithm based on multi-features fusion is studied. Complementary static and motion features are extracted and fused by exploiting local descriptors and holistic features as well as temporal and spatial features. Thus the influence of variable factors is reduced and the robustness of feature representation is improved. (2) For action modeling, non-linear SVM (support vector machine) action model learning algorithm based on MKL (multiple kernel learning) is firstly studied. Then additional action model learning algorithm based on transfer learning is studied to improve the extensibility of action models. (3) For action recognition, coarse action classification algorithm based on template matching of key poses is firstly studied to address the key poses mined from the action sequence. Then fine action classification algorithm based on non-linear MKL-based SVM is studied to address the spatiotemporal specialty in the sub video of key frames. In addit

英文关键词： Action recognition；multi-features fusion；machine learning；multi-levels classification；multi-patterns classification