NDLI: Fusing Multiple Features for Depth-Based Action Recognition

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ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

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Visual Understanding with RGB-D Sensors: An Introduction to the Special Issue

Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework

Depth Error Elimination for RGB-D Cameras

A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Robust Multiview Feature Learning for RGB-D Image Understanding

From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models

Accurate and Robust Moving-Object Segmentation for Telepresence Systems

Fusing Multiple Features for Depth-Based Action Recognition

An Evaluation of Gamesourced Data for Human Pose Estimation

Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect

A Real-Time Hand Posture Recognition System Using Deep Neural Networks

Real-Time System for Driver Fatigue Detection by RGB-D Camera

An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment

Exploring Spatial Correlation for Visual Object Retrieval

Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks

An Association-Based Unified Framework for Mining Features and Opinion Words

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population

Issue 1, April 2015

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 5

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ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

Fusing Multiple Features for Depth-Based Action Recognition

Content Provider	ACM Digital Library
Author	Chen, Wenbin Guo, Guodong Zhu, Yu
Copyright Year	2015
Abstract	Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the three-dimensional (3D) depth data captured by the emerging RGB-D sensors. Several features and/or algorithms have been proposed for depth-based action recognition. A question is raised: Can we find some complementary features and combine them to improve the accuracy significantly for depth-based action recognition? To address the question and have a better understanding of the problem, we study the fusion of different features for depth-based action recognition. Although data fusion has shown great success in other areas, it has not been well studied yet on 3D action recognition. Some issues need to be addressed, for example, whether the fusion is helpful or not for depth-based action recognition, and how to do the fusion properly. In this article, we study different fusion schemes comprehensively, using diverse features for action characterization in depth videos. Two different levels of fusion schemes are investigated, that is, feature level and decision level. Various methods are explored at each fusion level. Four different features are considered to characterize the depth action patterns from different aspects. The experiments are conducted on four challenging depth action databases, in order to evaluate and find the best fusion methods generally. Our experimental results show that the four different features investigated in the article can complement each other, and appropriate fusion methods can improve the recognition accuracies significantly over each individual feature. More importantly, our fusion-based action recognition outperforms the state-of-the-art approaches on these challenging databases.
Starting Page	1
Ending Page	20
Page Count	20
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2629483
Volume Number	6
Issue Number	2
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-31
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	4D descriptor RGB-D sensor Action recognition Data fusion Decision level Depth maps Feature level Feature selection Skeleton Spatiotemporal features
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

RGB-D Data-Based Action Recognition: A Review

Infrared and 3D Skeleton Feature Fusion for RGB-D Action Recognition

Transformation classification of human squat/sit-to-stand based on multichannel information fusion

Human action recognition framework by fusing multiple features

Multiple Feature Fusion Based Hand-held Object Recognition with RGB-D data

Collecting public RGB-D datasets for human daily activity recognition

Human interaction recognition fusing multiple features of depth sequences

Robust Human Activity Recognition Using Multimodal Feature-Level Fusion

Global Co-Occurrence Feature and Local Spatial Feature Learning for Skeleton-Based Action Recognition

Fusing Multiple Features for Depth-Based Action Recognition

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RGB-D Data-Based Action Recognition: A Review

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Transformation classification of human squat/sit-to-stand based on multichannel information fusion

Human action recognition framework by fusing multiple features

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