.png){kind=icon}
返回
Multiview Skeletal Interaction Recognition Using Active Joint Interaction Graph
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2016.2614228.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper addresses the problem of recognizing human skeletal interactions using multiview data captured from depth sensors. The interactions among people are important cues for group and crowd human behavior analysis. In this paper, we focus on modeling the person-person skeletal interactions for human activity recognition. First, we propose a novel graph model in each single-view case to encode class-specific person-person interaction patterns. Particularly, we model each person-person interaction by an attributed graph, which is designed to preserve the complex spatial structure among skeletal joints according to their activity levels as well as the spatio-temporal joint features. Then, combining the graph models for each single-view case, we propose the multigraph model to characterize each multiview interaction. Finally, we apply a general multiple kernel learning method to determine the optimal kernel weights for the proposed multigraph model while the optimal classifier is jointly learned. We evaluate the proposed approach on the M-2 I dataset, the SBU Kinect interaction dataset, and our interaction dataset. The experimental results show that our proposed approach outperforms several existing interaction recognition methods.
Keyword:
Activity analysis
depth sensor
graph-based modeling
graph kernel
human interaction recognition
multiview
multiple kernel learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.7
论文数:
4.5K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
Time-Dependent Global Sensitivity Analysis for Long-Term Degeneracy Model Using Polynomial Chaos
OAAI
OAAI分享收藏
分享收藏分享收藏
查看更多内容