Efficient Pose-Based Action Recognition

被引：40

作者：

Eweiwi, Abdalrahman ^{[1
]}

Cheema, Muhammed S. ^{[1
]}

Bauckhage, Christian ^{[1
,3
]}

Gall, Juergen ^{[2
]}

机构：

[1] Univ Bonn, Bonn Aachen Int Ctr IT, Bonn, Germany

[2] Univ Bonn, Comp Vis Grp, Bonn, Germany

[3] Fraunhofer IAIS, Multimedia Pattern Recognit Grp, St Augustin, Germany

来源：

COMPUTER VISION - ACCV 2014, PT V | 2015年 / 9007卷

关键词：

DENSE;

D O I：

10.1007/978-3-319-16814-2_28

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Action recognition from 3d pose data has gained increasing attention since the data is readily available for depth or RGB-D videos. The most successful approaches so far perform an expensive feature selection or mining approach for training. In this work, we introduce an algorithm that is very efficient for training and testing. The main idea is that rich structured data like 3d pose does not require sophisticated feature modeling or learning. Instead, we reduce pose data over time to histograms of relative location, velocity, and their correlations and use partial least squares to learn a compact and discriminative representation from it. Despite of its efficiency, our approach achieves state-of-the-art accuracy on four different benchmarks. We further investigate differences of 2d and 3d pose data for action recognition.

引用

页码：428 / 443

页数：16