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Recognizing human interactions by genetic algorithm-based random forest spatio-temporal correlation

Content Provider	Springer Nature Link
Author	Li, Nijun Cheng, Xu Guo, Haiyan Wu, Zhenyang
Copyright Year	2015
Abstract	Recognizing human interactions is a more challenging task than recognizing single person activities and has attracted much attention of the computer vision community. This paper proposes an innovative and effective way to recognize human interactions, which incorporates the advantages of both global motion context (MC) feature and spatio-temporal (S-T) correlation of local spatio-temporal interest point feature. The MC feature is used to train a random forest where genetic algorithm (GA) is applied to the training phase to achieve a good compromise between reliability and efficiency. Besides, we propose S-T correlation-based match, where MC’s structure and Needleman–Wunsch algorithm are used to calculate the spatial and temporal correlation score of two videos, respectively. Experiments on the UT-Interaction dataset show that our approaches outperform other prevalent machine learning methods, and that the combination of GA search-based random forest and S-T correlation achieves the state-of-the-art performance.
Starting Page	267
Ending Page	282
Page Count	16
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	19
Issue Number	1
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2015-03-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Motion context (MC) Spatio-temporal interest points (STIPs) Random forest Genetic algorithm (GA) Spatio-temporal (S-T) correlation Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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

A Hybrid Method for Human Interaction Recognition Using Spatio-temporal Interest Points

GA-STIP: Action Recognition in Multi-Channel Videos With Geometric Algebra Based Spatio-Temporal Interest Points

Fusing $${\mathcal {R}}$$ Features and Local Features with Context-Aware Kernels for Action Recognition

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Recognizing human interactions by genetic algorithm-based random forest spatio-temporal correlation

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