Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction
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- 作者：Gang Yu (17)
Zicheng Liu (18)
Junsong Yuan (17)
17. School of Electrical and Electronic Engineering ; Nanyang Technological University ; Singapore ; Singapore
18. Microsoft Research ; Redmond ; WA ; USA - 刊名：Lecture Notes in Computer Science
- 出版年：2015
- 出版时间：2015
- 年：2015
- 卷：9007
- 期：1
- 页码：50-65
- 全文大小：733 KB
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- 丛书名：978-3-319-16813-5
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
文摘
This paper presents a novel visual representation, called orderlets, for real-time human action recognition with depth sensors. An orderlet is a middle level feature that captures the ordinal pattern among a group of low level features. For skeletons, an orderlet captures specific spatial relationship among a group of joints. For a depth map, an orderlet characterizes a comparative relationship of the shape information among a group of subregions. The orderlet representation has two nice properties. First, it is insensitive to small noise since an orderlet only depends on the comparative relationship among individual features. Second, it is a frame-level representation thus suitable for real-time online action recognition. Experimental results demonstrate its superior performance on online action recognition and cross-environment action recognition.