Action Recognition Using Motion Primitives and Probabilistic Edit Distance

2006 ◽  
pp. 375-384 ◽  
Author(s):  
P. Fihl ◽  
M. B. Holte ◽  
T. B. Moeslund ◽  
L. Reng
Keyword(s):  
Action Recognition ◽  
Edit Distance ◽  
Motion Primitives

Motion Primitives and Probabilistic Edit Distance for Action Recognition

2009 ◽  
pp. 24-35 ◽  
Author(s):  
Preben Fihl ◽  
Michael B. Holte ◽  
Thomas B. Moeslund
Keyword(s):  
Action Recognition ◽  
Edit Distance ◽  
Motion Primitives

A Novel Human Action Recognition Algorithm Based on Edit Distance

2011 ◽  
Vol 186 ◽  
pp. 261-265
Author(s):  
He Jin Yuan ◽  
Cui Ru Wang
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Edit Distance ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Action Sequence ◽  
Competitive Neural Network ◽  
Training Samples ◽  
Symbolic Sequences

A novel human action recognition algorithm based on edit distance is proposed in this paper. In the method, the mesh feature of each image in human action sequence is firstly calculated; then the feature vectors are quantized through a rival penalized competitive neural network; and through this processing, the time-sequential image sequences are converted into symbolic sequences. For human action recognition, the observed action is firstly vector quantized with the former competitive neural network; then the normalized edit distances to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can reach above 94%.

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

Stochastic Contextual Edit Distance and Probabilistic FSTs

2014 ◽  
Author(s):  
Ryan Cotterell ◽  
Nanyun Peng ◽  
Jason Eisner
Keyword(s):  
Edit Distance

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

Vertical View Human Action Recognition from Range Images

2019 ◽  
pp. 1342
Author(s):  
Akinobu Watanabe ◽  
Keiichi Mitani
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Range Images

Vertical View Human Action Recognition from Range Images

2019 ◽  
pp. 1342
Author(s):  
Akinobu Watanabe ◽  
Keiichi Mitani
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Range Images
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