An effective approach for human actions recognition based on optical flow and edge features

2012 International Conference on Control, Automation and Information Sciences (ICCAIS) ◽

10.1109/iccais.2012.6466597 ◽

2012 ◽

Cited By ~ 2

Author(s):

Viet Vo ◽

Ngoc Ly

Keyword(s):

Optical Flow ◽

Human Actions ◽

Edge Features ◽

Human Actions Recognition

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Human actions recognition using bag of optical flow words

10.1117/12.954130 ◽

2012 ◽

Author(s):

Xu Zhang ◽

Zhenjiang Miao ◽

Lili Wan

Keyword(s):

Optical Flow ◽

Human Actions ◽

Human Actions Recognition

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Comparing alternatives for capturing dynamic information in Bag-of-Visual-Features approaches applied to human actions recognition

2009 IEEE International Workshop on Multimedia Signal Processing ◽

10.1109/mmsp.2009.5293303 ◽

2009 ◽

Cited By ~ 2

Author(s):

Ana Paula B. Lopes ◽

Rodrigo Silva Oliveira ◽

Jussara M. de Almeida ◽

Arnaldo de A. Araujo

Keyword(s):

Visual Features ◽

Dynamic Information ◽

Human Actions ◽

Human Actions Recognition

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Human actions recognition: an approach based on stable motion boundary fields

Multimedia Tools and Applications ◽

10.1007/s11042-017-5477-0 ◽

2017 ◽

Vol 77 (16) ◽

pp. 20715-20729 ◽

Cited By ~ 1

Author(s):

Imen Lassoued ◽

Ezzeddine Zagrouba

Keyword(s):

Human Actions ◽

Stable Motion ◽

Boundary Fields ◽

Human Actions Recognition ◽

Motion Boundary

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Human actions recognition on multimedia hardware using angle-based and coordinate-based features and multivariate continuous hidden Markov model classifier

Multimedia Tools and Applications ◽

10.1007/s11042-015-2928-3 ◽

2015 ◽

Vol 75 (23) ◽

pp. 16265-16285 ◽

Cited By ~ 5

Author(s):

Tomasz Hachaj ◽

Marek R. Ogiela

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Human Actions ◽

Human Actions Recognition

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Application of Neural Network for Human Actions Recognition

Communications in Computer and Information Science - Computational Intelligence and Intelligent Systems ◽

10.1007/978-981-10-0356-1_18 ◽

2016 ◽

pp. 183-191

Author(s):

Tomasz Hachaj ◽

Marek R. Ogiela

Keyword(s):

Neural Network ◽

Human Actions ◽

Human Actions Recognition

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Optical flow estimation combining with objects edge features

Electronics Letters ◽

10.1049/el.2020.0578 ◽

2020 ◽

Vol 56 (13) ◽

pp. 661-663

Author(s):

Bin Liao ◽

Jinlong Hu ◽

Tenghui Li

Keyword(s):

Optical Flow ◽

Flow Estimation ◽

Optical Flow Estimation ◽

Edge Features

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Shape –Based Human Actions Recognition in Videos

Human-Computer Interaction. Design and Development Approaches - Lecture Notes in Computer Science ◽

10.1007/978-3-642-21602-2_58 ◽

2011 ◽

pp. 539-546 ◽

Cited By ~ 3

Author(s):

Nitish Amraji ◽

Lin Mu ◽

Mariofanna Milanova

Keyword(s):

Human Actions ◽

Human Actions Recognition

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Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions

2009 IEEE Conference on Computer Vision and Pattern Recognition ◽

10.1109/cvprw.2009.5206821 ◽

2009 ◽

Cited By ~ 20

Author(s):

R. Chaudhry ◽

A. Ravichandran ◽

G. Hager ◽

R. Vidal

Keyword(s):

Dynamical Systems ◽

Optical Flow ◽

Nonlinear Dynamical Systems ◽

Human Actions ◽

Nonlinear Dynamical

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Research on Applied Technology in Human Action Recognition Based on Skeleton Information

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.859.498 ◽

2013 ◽

Vol 859 ◽

pp. 498-502 ◽

Cited By ~ 1

Author(s):

Zhi Qiang Wei ◽

Ji An Wu ◽

Xi Wang

Keyword(s):

Data Analysis ◽

Action Recognition ◽

Recognition Rate ◽

Human Action Recognition ◽

Human Action ◽

Experimental Results ◽

Human Actions ◽

Applied Technology ◽

Feature Sequence ◽

Human Actions Recognition

In order to realize the identification of human daily actions, a method of identifying human daily actions is realized in this paper, which transforms this problem into converting human action recognition into analyzing feature sequence. Then the feature sequence combined with improved LCS algorithm could realize the human actions recognition. Data analysis and experimental results show the recognition rate of this method is high and speed is fast, and this applied technology will have broad prospects.

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Human Action Recognition Based on Improved MIL

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.2152 ◽

2015 ◽

Vol 713-715 ◽

pp. 2152-2155 ◽

Cited By ~ 1

Author(s):

Shao Ping Zhu

Keyword(s):

Computer Vision ◽

Action Recognition ◽

Heuristic Algorithm ◽

Human Action Recognition ◽

Human Action ◽

Image Sequences ◽

Multiple Instance Learning ◽

New Method ◽

Human Actions ◽

Human Actions Recognition

According to the problem that achieves robust human actions recognition from image sequences in computer vision, using the Iterative Querying Heuristic algorithm as a guide, a improved Multiple Instance Learning (MIL) method is proposed for human action recognition in video image sequences. Experiments show that the new method can quickly recognize human actions and achieve high recognition rates, and on the Weizmann database validate our analysis.

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