Real-time human action recognition based on motion shapes

2014 ◽  
Author(s):  
Thierry Pinheiro Moreira
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

scholarly journals A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Alexandros Andre Chaaraoui ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Assisted Living ◽  
Learning Algorithm ◽  
Ambient Assisted Living ◽  
Human Action Recognition ◽  
Human Action ◽  
Sequence Matching ◽  
Low Dimensional ◽  
Video Frequency

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

Smart Surveillance and Real-Time Human Action Recognition Using OpenPose

2020 ◽  
pp. 504-509
Author(s):  
Vatsal Rathod ◽  
Rishvanth Katragadda ◽  
Saurabh Ghanekar ◽  
Saurav Raj ◽  
Pushyamitra Kollipara ◽  
...  
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Smart Surveillance

scholarly journals Real-Time Human Action Recognition using Stacked Sparse Autoencoders

2018 ◽  
Vol 11 (4) ◽  
pp. 1-6
Author(s):  
Adnan Farooq ◽  
Emad U Din Mohammad ◽  
Abdullah Ahmad Zarir ◽  
Amelia Ritahani Ismail ◽  
Suriani Sulaiman ◽  
...  
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Stacked Sparse Autoencoders

scholarly journals 2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling

2020 ◽  
Vol 22 (6) ◽  
pp. 1433-1446 ◽  
Author(s):  
Federico Angelini ◽  
Zeyu Fu ◽  
Yang Long ◽  
Ling Shao ◽  
Syed Mohsen Naqvi
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Occlusion Handling

A lattice-based neuro-computing methodology for real-time human action recognition

2011 ◽  
Vol 181 (10) ◽  
pp. 1874-1887 ◽  
Author(s):  
Vassilis Syrris ◽  
Vassilios Petridis
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Computing Methodology ◽  
Human Action Recognition ◽  
Human Action

Continuous human action recognition in real time

2012 ◽  
Vol 68 (3) ◽  
pp. 827-844 ◽  
Author(s):  
Ping Guo ◽  
Zhenjiang Miao ◽  
Yuan Shen ◽  
Wanru Xu ◽  
Dianyong Zhang
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
Sign in / Sign up

Export Citation Format

Share Document