scholarly journals View-Independent Action Recognition from Temporal Self-Similarities

2011 ◽  
Vol 33 (1) ◽  
pp. 172-185 ◽  
Author(s):  
I N Junejo ◽  
E Dexter ◽  
I Laptev ◽  
Patrick Pérez
Keyword(s):  
Action Recognition ◽  
Independent Action

Bayesian Covariance Representation with Global Informative Prior for 3D Action Recognition

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Jianhai Zhang ◽  
Zhiyong Feng ◽  
Yong Su ◽  
Meng Xing
Keyword(s):  
Action Recognition ◽  
Temporal Order ◽  
State Of The Art ◽  
Feature Representation ◽  
Independent Action ◽  
Sufficient Statistics ◽  
Informative Prior ◽  
Global Action ◽  
High Order Statistics ◽  
Order Structures

For the merits of high-order statistics and Riemannian geometry, covariance matrix has become a generic feature representation for action recognition. An independent action can be represented by an empirical statistics over all of its pose samples. Two major problems of covariance include the following: (1) it is prone to be singular so that actions fail to be represented properly, and (2) it is short of global action/pose-aware information so that expressive and discriminative power is limited. In this article, we propose a novel Bayesian covariance representation by a prior regularization method to solve the preceding problems. Specifically, covariance is viewed as a parametric maximum likelihood estimate of Gaussian distribution over local poses from an independent action. Then, a Global Informative Prior (GIP) is generated over global poses with sufficient statistics to regularize covariance. In this way, (1) singularity is greatly relieved due to sufficient statistics, (2) global pose information of GIP makes Bayesian covariance theoretically equivalent to a saliency weighting covariance over global action poses so that discriminative characteristics of actions can be represented more clearly. Experimental results show that our Bayesian covariance with GIP efficiently improves the performance of action recognition. In some databases, it outperforms the state-of-the-art variant methods that are based on kernels, temporal-order structures, and saliency weighting attentions, among others.

Hierarchical Hough forests for view-independent action recognition

2016 ◽  
Author(s):  
Barbara Hilsenbeck ◽  
David Munch ◽  
Hilke Kieritz ◽  
Wolfgang Hubner ◽  
Michael Arens
Keyword(s):  
Action Recognition ◽  
Independent Action ◽  
Hough Forests

Viewpoint Independent Action Recognition

2008 ◽  
Vol 19 (7) ◽  
pp. 1623-1634 ◽  
Author(s):  
Fei-Yue HUANG
Keyword(s):  
Action Recognition ◽  
Independent Action

View-independent action recognition: a hybrid approach

2015 ◽  
Vol 75 (12) ◽  
pp. 6755-6775 ◽  
Author(s):  
Seyed Mohammad Hashemi ◽  
Mohammad Rahmati
Keyword(s):  
Action Recognition ◽  
Hybrid Approach ◽  
Independent Action

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

Surgical Action Recognition with Spatiotemporal Convolutional Neural Networks

2019 ◽  
Author(s):  
Giacomo De Rossi ◽  
◽  
Nicola Piccinelli ◽  
Francesco Setti ◽  
Riccardo Muradore ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition
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