基于深度和惯性传感器的多视角柯西估计特征嵌入人体动作识别
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition
摘要：
Kinect和惯性传感器的日益普及促使人们对人类动作识别进行了深入的研究。由于人的动作可以通过从Kinect和惯性传感器中提取的多个特征表示来表征，因此必须将多视图特征编码到用于人的动作识别的统一空间中。在本文中，我们提出了一种新的无监督特征融合方法称为多视图柯西估计特征嵌入（MCEFE）的人体动作识别。通过最小化经验风险，MCEFE集成多个视图中的编码互补信息，以找到统一的数据表示和投影矩阵。为了增强对异常值的鲁棒性，对重构误差施加Cauchy估计。此外，集成流形正则化的投影矩阵上进行编码不同的视图之间的相关性，并避免过拟合。在中国科学院-云南大学-多模态人体动作数据库（CAS-YNU-MHAD）上进行了实验，验证了MCEFE对人体动作识别的有效性和鲁棒性。
英文摘要：
The ever-growing popularity of Kinect and inertial sensors has prompted intensive research efforts on human action recognition. Since human actions can be characterized by multiple feature representations extracted from Kinect and inertial sensors, multiview features must be encoded into a unified space optimal for human action recognition. In this paper, we propose a new unsupervised feature fusion method termed Multiview Cauchy Estimator Feature Embedding (MCEFE) for human action recognition. By minimizing empirical risk, MCEFE integrates the encoded complementary information in multiple views to find the unified data representation and the projection matrices. To enhance robustness to outliers, the Cauchy estimator is imposed on the reconstruction error. Furthermore, ensemble manifold regularization is enforced on the projection matrices to encode the correlations between different views and avoid overfitting. Experiments are conducted on the new Chinese Academy of Sciences - Yunnan University - Multimodal Human Action Database (CAS-YNU-MHAD) to demonstrate the effectiveness and robustness of MCEFE for human action recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
