4.6 Article

CuDi3D: Curvilinear displacement based approach for online 3D action detection

期刊

COMPUTER VISION AND IMAGE UNDERSTANDING
卷 174, 期 -, 页码 57-69

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2018.07.003

关键词

Online action recognition; Skeleton-based approach; Human action detection; Curvilinear displacement; Online segmentation; Skeleton data stream

向作者/读者索取更多资源

Being able to interactively detect and recognize 3D actions based on skeleton data, in unsegmented streams, has become an important computer vision topic. It raises three scientific problems in relation with variability. The first one is the temporal variability that occurs when subjects perform gestures with different speeds. The second one is the inter-class spatial variability, which refers to disparities between the displacement amounts induced by different classes (i.e. long vs. short movements). The last one is the intra-class spatial variability caused by differences in style and gesture amplitude. In this paper, we design an original approach that better considers these three issues. To address temporal variability we introduce the notion of curvilinear segmentation. It consists in extracting features, not on temporally-based sliding windows, but on trajectory segments for which the cumulated displacement equals a class-based amount. Second, to tackle inter-class spatial variability, we define several competing classifiers with their dedicated curvilinear windows. Last, we address intra-class spatial variability by designing a fusion system that takes the decisions and confidence scores of every competing classifier into account. Extensive experiments on four challenging skeleton-based datasets demonstrate the relevance of the proposed approach for action recognition and online action detection.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据