基于动作子空间和权重条件随机场的行为识别王智文

针对单目视频中的人类行为识别，提出了基于动作子空间与权重条件随机场的行为识别方法。该方法结合了基于特征提取的核主分量分析 (KPCA) 与基于运动建模的权重条件随机场 (WCRF) 模型。探讨了通过非线性降维行为空间的基本结构，并在运动轨迹投影过程中保留清晰的时间顺序，使人体轮廓数据表示更紧凑。WCRF通过多种交互途径对时间序列建模，从而提高了信息共享的联合精确度，具有超越生成模型的优势 (如放宽观察之间独立性的假设，有效地将重叠的特征和远距离依存关系合并起来的能力)。实验结果表明，该行为识别方法不仅能够准确地识别随时间、区域内外人员变化的人类行为，而且对噪声和其他因素鲁棒性强。

Behavior Recognition Based on Action Subspace and Weight Condition Random Field

For human behavior recognition in monocular video, a method for recognizing human behavior based on action subspace and weighted condition random field is presented in this paper. This method combines kernel principal component analysis (KPCA) based on feature extraction and weighted conditional random field (WCRF) based on activity modeling. Silhouette data of human is represented more compactly by nonlinear dimensionality reduction that explores the basic structure of action space and preserves explicit temporal orders in the course of projection trajectories of motions. Temporal sequences are modeled in WCRF by using multiple interacting ways, thus increasing joint accuracy by information sharing, and this model has superiority over generative ones (e.g., relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies). The experimental results show that the proposed behavior recognition method can not only accurately recognize human activities with temporal, external and internal person variations, but also considerably robust to noise and other factors.