Human action recognition based on two-stream Ind recurrent neural network

2019 ◽  
Author(s):  
Penghua Ge ◽  
Min Zhi
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Recurrent Neural Network ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Recurrent Neural Network for Human Action Recognition using Star Skeletonization

2019 ◽  
pp. 335-344
Author(s):  
Anantha Prabha P ◽  
Srimathi R ◽  
Srividhya R ◽  
Sowmiya T G
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Recurrent Neural Network ◽  
Feature Vector ◽  
Video Retrieval ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Computer Interactions ◽  
Vector Sequence ◽  
Active Research

Human Action Recognition has been an active research topic since early 1980s due to its promising applications in many domains like video indexing, surveillance, gesture recognition, video retrieval and human-computer interactions where the actions in the form of videos or sensor datas are recognized. The extraction of relevant features from the video streams is the most challenging part. With the emergence of advanced artificial intelligence techniques, deep learning methods are adopted to achieve the goal. The proposed system presents a Recurrent Neural Network (RNN) methodology for Human Action Recognition using star skeleton as a representative descriptor of human posture. Star skeleton is the process of jointing the gross contour extremes of a body to its centroid. To use star skeleton as feature for action recognition, the feature is defined as a five-dimensional vector in star fashion because the head and four limbs are usually local extremes of human body. In our project, we assumed an action is composed of a series of star skeletons overtime. Therefore, images expressing human action which are time-sequential are transformed into a feature vector sequence. Then the feature vector sequence must be transformed into symbol sequence so that RNN can model the action. RNN is used because the features extracted are time dependent

scholarly journals Convolutional and Recurrent Neural Network for Human Action Recognition: application on American Sign Language

2019 ◽  
Author(s):  
Hernandez Vincent ◽  
Suzuki Tomoya ◽  
Venture Gentiane
Keyword(s):  
Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Action Recognition ◽  
Recurrent Neural Network ◽  
Data Augmentation ◽  
Human Action Recognition ◽  
Human Action ◽  
American Sign ◽  
Test Set

AbstractHuman Action Recognition (HAR) is an important and difficult topic because of the important variability between tasks repeated several times by a subject and between subjects. This work is motivated by providing time-series signal classification and a robust validation and test approaches. This study proposes to classify 60 American Sign Language signs from data provided by the LeapMotion sensor by using a combined approach with Convolutional Neural Network (ConvNet) and Recurrent Neural Network with Long-Short Term Memory cells (LSTM) called ConvNet-LSTM. Moreover, a complete kinematic model of the right and left forearm/hand/fingers/thumb is proposed as well as the use of a simple data augmentation technique to improve the generalization of neural networks. Results showed an accuracy of 89.3% on a user-independent test set with data augmentation when using the ConvNet-LSTM, while LSTM alone provided an accuracy of 85.0% on the same test set. The result dropped respectively to 85.9% and 81.4% without data augmentation.

scholarly journals I3D-Shufflenet Based Human Action Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 301
Author(s):  
Guocheng Liu ◽  
Caixia Zhang ◽  
Qingyang Xu ◽  
Ruoshi Cheng ◽  
Yong Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Convolution Kernel ◽  
Histogram Of Oriented Gradients ◽  
Temporal Features ◽  
Convolution Kernels

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.

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