论文标题
子空间聚类以进行协方差表示和时间修剪以进行行动识别
Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
论文作者
论文摘要
本文解决了人类行动识别的问题，该问题定义为从骨骼数据中以修剪序列显示哪种动作的分类。尽管为本应用设计的最先进的方法都受到监督，但在本文中，我们追求一个更具挑战性的方向：通过无监督的学习解决问题。为此，我们提出了一种新颖的子空间聚类方法，该方法利用协方差矩阵来增强动作的可区分性和时间戳修剪方法，使我们能够更好地处理数据的时间维度。通过广泛的实验验证，我们表明我们的计算管道超过了现有的无监督方法，但与监督方法相比，还可以导致有利的性能。
This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data. Albeit state-of-the-art approaches designed for this application are all supervised, in this paper we pursue a more challenging direction: Solving the problem with unsupervised learning. To this end, we propose a novel subspace clustering method, which exploits covariance matrix to enhance the action's discriminability and a timestamp pruning approach that allow us to better handle the temporal dimension of the data. Through a broad experimental validation, we show that our computational pipeline surpasses existing unsupervised approaches but also can result in favorable performances as compared to supervised methods.
