Human action recognition with spatial-temporal feature filter

2011 ◽  
Author(s):  
Hong-Bo Zhang ◽  
Shao-Zi Li ◽  
Li-Ying Wang ◽  
Da Lu ◽  
Qi-An Chen ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Temporal Feature

Human Action Recognition Using Spatio-Temporal Multiplier Network and Attentive Correlated Temporal Feature

2021 ◽  
Author(s):  
C. Indhumathi ◽  
V. Murugan ◽  
G. Muthulakshmii
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Human Action Recognition ◽  
Human Action ◽  
Regional Correlation ◽  
Temporal Features ◽  
Adaptive Motion ◽  
Spatio Temporal ◽  
Inter Frame ◽  
Temporal Feature

Nowadays, action recognition has gained more attention from the computer vision community. Normally for recognizing human actions, spatial and temporal features are extracted. Two-stream convolutional neural network is used commonly for human action recognition in videos. In this paper, Adaptive motion Attentive Correlated Temporal Feature (ACTF) is used for temporal feature extractor. The temporal average pooling in inter-frame is used for extracting the inter-frame regional correlation feature and mean feature. This proposed method has better accuracy of 96.9% for UCF101 and 74.6% for HMDB51 datasets, respectively, which are higher than the other state-of-the-art methods.

scholarly journals Human action recognition based on deep network and feature fusion

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4967-4974
Author(s):  
Dongli Wang ◽  
Jun Yang ◽  
Yan Zhou ◽  
Zhen Zhou
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Human Action Recognition ◽  
Human Action ◽  
Feature Representation ◽  
Support Vector ◽  
Motion Information ◽  
Global Motion ◽  
Difference Images ◽  
Temporal Feature

Feature representation is of vital importance for human action recognition. In recent few years, the application of deep learning in action recognition has become popular. However, for action recognition in videos, the advantage of single convolution feature over traditional methods is not so evident. In this paper, a novel feature representation that combines spatial and temporal feature with global motion information is proposed. Specifically, spatial and temporal feature from RGB images is extracted by convolutional neural network (CNN) and long short-term memory (LSTM) network. On the other hand, global motion information extracted from motion difference images using another separate CNN. Hereby, the motion difference images are binary video frames processed by exclusive or (XOR). Finally, support vector machine (SVM) is adopted as classifier. Experimental results on YouTube Action and UCF-50 show the superiority of the proposed method.

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
Sign in / Sign up

Export Citation Format

Share Document