Evaluation of Local Spatio-temporal Features for Action Recognition
Evaluation of Local Spatio-temporal Features for Action Recognition
复制标题
DOI：
10.5244/c.23.124
复制
发表时间：
2009-09
期刊：
影响因子：
--
通讯作者：
Heng Wang;M. M. Ullah-M.;Alexander Kläser;I. Laptev;C. Schmid
中科院分区：
文献类型：
--
作者：
Heng Wang;M. M. Ullah-M.;Alexander Kläser;I. Laptev;C. Schmid
局部时空特征最近成为一种流行的视频表示的动作识别。文献中已经提出了几种特征定位和描述的方法，并且对于一些动作类已经证明了有希望的识别结果。然而，现有方法的比较往往是有限的，因为所使用的实验设置不同。本文的目的是在常见的实验装置中评估和比较之前提出的时空特征。特别是，我们考虑了四个不同的特征检测器和六个本地特征描述符，并使用标准的袋特征SVM方法进行动作识别。我们研究了这些方法在分布在三个不同难度的数据集上的25个动作类上的性能。在有趣的结论中，我们证明了时空特征的定期采样始终优于所有测试的时空兴趣点检测器在现实环境中的人类行为。我们还展示了不同数据集上大多数方法的一致性排名，并讨论了它们的优点和局限性。
Local space-time features have recently become a popular video representation for action recognition. Several methods for feature localization and description have been proposed in the literature and promising recognition results were demonstrated for a number of action classes. The comparison of existing methods, however, is often limited given the different experimental settings used. The purpose of this paper is to evaluate and compare previously proposed space-time features in a common experimental setup. In particular, we consider four different feature detectors and six local feature descriptors and use a standard bag-of-features SVM approach for action recognition. We investigate the performance of these methods on a total of 25 action classes distributed over three datasets with varying difficulty. Among interesting conclusions, we demonstrate that regular sampling of space-time features consistently outperforms all tested space-time interest point detectors for human actions in realistic settings. We also demonstrate a consistent ranking for the majority of methods over different datasets and discuss their advantages and limitations.