NDLI: Action recognition via multi-feature fusion and Gaussian process classification

Content Provider	IEEE Xplore Digital Library
Author	Liang Wang Hang Zhou Sui-Chien Low Leckie, C.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia (Liang Wang; Hang Zhou; Sui-Chien Low; Leckie, C.)
Abstract	Human action analysis has recently received growing interest from vision researchers. In this paper, we present a simple but effective approach for action recognition by combining multiple complementary features with Gaussian process classification. Since it is often insufficient for a single type of feature derived from action videos to characterize variations among different motions, we propose to combine two types of features for better action representation, namely the quantized vocabulary of spatio-temporal (ST) volumes and the quantized vocabulary of silhouette projection (SP) histograms, capturing the global and local motions as well as the appearances of the actions respectively. The use of such features also elegantly converts the original temporal classification problem into a static classification problem, which enables the use of any existing classifiers. Besides the traditional K-nearest neighbor (KNN), we use the state-of-the-art Gaussian process (GP) model for achieving stable performance. We also investigate how the number of features can be reduced while still maintaining classification accuracy. Our experimental results have shown that the fusion of multiple features improved the recognition accuracy compared with the use of any single feature type. We also show that the redundancy of fused features can be reduced by spectral feature analysis. Compared with KNN, the use of the GP model significantly improved the stability of the algorithm's performance with respect to the parameter settings.
Starting Page	1
Ending Page	6
File Size	362101
Page Count	6
File Format	PDF
ISBN	9781424454976
ISSN	15505790
e-ISBN	9781424454983
DOI	10.1109/WACV.2009.5403113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian processes Humans Vocabulary Feature extraction Shape Histograms Videos Lighting Trajectory Computer science
Content Type	Text
Resource Type	Article

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

Feature seeding for action recognition

Exploiting Visual Cues in Non-Scripted Lecture Videos for Multi-modal Action Recognition

Hierarchical Latent Dirichlet Allocation models for realistic action recognition

Human action recognition via sum-rule fusion of fuzzy K-Nearest Neighbor classifiers

3D trajectories for action recognition

Keep it accurate and diverse: Enhancing action recognition performance by ensemble learning

Combining gradient histograms using orientation tensors for human action recognition

Hierarchical Filtered Motion for Action Recognition in Crowded Videos

Action Recognition Using Space-Time Shape Difference Images

Action recognition via multi-feature fusion and Gaussian process classification

Similar Documents

Feature seeding for action recognition

Exploiting Visual Cues in Non-Scripted Lecture Videos for Multi-modal Action Recognition

Hierarchical Latent Dirichlet Allocation models for realistic action recognition

Human action recognition via sum-rule fusion of fuzzy K-Nearest Neighbor classifiers

3D trajectories for action recognition

Keep it accurate and diverse: Enhancing action recognition performance by ensemble learning

Combining gradient histograms using orientation tensors for human action recognition

Hierarchical Filtered Motion for Action Recognition in Crowded Videos

Action Recognition Using Space-Time Shape Difference Images

Action recognition via multi-feature fusion and Gaussian process classification