scholarly journals View-Invariant Deep Architecture for Human Action Recognition Using Two-Stream Motion and Shape Temporal Dynamics

2020 ◽  
Vol 29 ◽  
pp. 3835-3844 ◽  
Author(s):  
Chhavi Dhiman ◽  
Dinesh Kumar Vishwakarma
Keyword(s):  
Action Recognition ◽  
Temporal Dynamics ◽  
Human Action Recognition ◽  
Human Action ◽  
Deep Architecture

scholarly journals A Bayesian Dynamical Approach for Human Action Recognition

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5613
Author(s):  
Amirreza Farnoosh ◽  
Zhouping Wang ◽  
Shaotong Zhu ◽  
Sarah Ostadabbas
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Human Action Recognition ◽  
Human Action ◽  
Superior Performance ◽  
Action Classification ◽  
Motion Data ◽  
Highly Correlated ◽  
Low Dimensional

We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the motion data and their associated action labels. We parameterize these temporal processes with regard to a switching deep autoregressive prior to accommodate both multimodal and higher-order nonlinear inter-dependencies. This results in a dynamical deep generative latent model that parses meaningful intrinsic states in skeletal dynamics and enables action recognition. These sequences of states provide visual and quantitative interpretations about motion primitives that gave rise to each action class, which have not been explored previously. In contrast to previous works, which often overlook temporal dynamics, our method explicitly model temporal transitions and is generative. Our experiments on two large-scale 3D skeletal datasets substantiate the superior performance of our model in comparison with the state-of-the-art methods. Specifically, our method achieved 6.3% higher action classification accuracy (by incorporating a dynamical generative framework), and 3.5% better predictive error (by employing a nonlinear second-order dynamical transition model) when compared with the best-performing competitors.

scholarly journals An Efficient Human Instance-Guided Framework for Video Action Recognition

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8309
Author(s):  
Inwoong Lee ◽  
Doyoung Kim ◽  
Dongyoon Wee ◽  
Sanghoon Lee
Keyword(s):  
Action Recognition ◽  
Temporal Dynamics ◽  
State Of The Art ◽  
Confusion Matrix ◽  
Human Action Recognition ◽  
Human Action ◽  
Stream Networks ◽  
Comparable Performance ◽  
Motion Features ◽  
Temporal Action

In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video action recognition. In contrast to traditional methods which generally used entire images, we propose a new human instance-level video action recognition framework. In this framework, we represent the instance-level features using human boxes and keypoints, and our action region features are used as the inputs of the temporal action head network, which makes our framework more discriminative. We also propose novel temporal action head networks consisting of various modules, which reflect various temporal dynamics well. In the experiment, the proposed models achieve comparable performance with the state-of-the-art approaches on two challenging datasets. Furthermore, we evaluate the proposed features and networks to verify the effectiveness of them. Finally, we analyze the confusion matrix and visualize the recognized actions at human instance level when there are several people.

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

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation

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.

Sign in / Sign up

Export Citation Format

Share Document