路径签名方法的发展及其在基于路标的人体动作识别中的应用
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition
摘要：
视频中基于地标的人体动作识别是计算机视觉中一个具有挑战性的课题。其中一个关键步骤是设计一个通用的方法，产生的空间结构和时间动态的歧视性功能。为此，我们把不断发展的地标数据作为一个高维路径，并应用非线性路径签名技术，提供一个富有表现力的，强大的，非线性的，和可解释的表示顺序事件。我们不从原始路径中提取签名特征，而是提出路径分解和路径变换作为预处理步骤。路径分解将高维路径线性地转换为低维路径的集合;其中一些路径位于姿态空间中，而另一些则定义在时间间隔的多尺度集合上。路径转换以标准方式用附加坐标装饰路径，以允许转换路径的截断签名暴露附加特征。对于空间表示，我们应用签名变换来矢量化姿态分解产生的路径，对于时间表示，我们再次应用它来描述这种不断发展的矢量化。最后，将所有特征集合在一起，构成线性单隐层全连接网络的输入向量进行分类。在四个数据集上的实验结果表明，仅使用线性浅层网络和Dropconnect的拟议特征集是有效的，并实现了与高级深层网络相当的最新结果，同时能够解释。
英文摘要：
Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for the spatial structure and temporal dynamics. To this end, we regard the evolving landmark data as a high-dimensional path and apply non-linear path signature techniques to provide an expressive, robust, non-linear, and interpretable representation for the sequential events. We do not extract signature features from the raw path, rather we propose path disintegrations and path transformations as preprocessing steps. Path disintegrations turn a high-dimensional path linearly into a collection of lower-dimensional paths; some of these paths are in pose space while others are defined over a multiscale collection of temporal intervals. Path transformations decorate the paths with additional coordinates in standard ways to allow the truncated signatures of transformed paths to expose additional features. For spatial representation, we apply the signature transform to vectorize the paths that arise out of pose disintegration, and for temporal representation, we apply it again to describe this evolving vectorization. Finally, all the features are collected together to constitute the input vector of a linear single-hidden-layer fully-connected network for classification. Experimental results on four datasets demonstrated that the proposed feature set with only a linear shallow network and Dropconnect is effective and achieves comparable state-of-the-art results to the advanced deep networks, and meanwhile, is capable of interpretation.
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。
