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 A Fine Grainedresearch Over Human Action Recognition

2019 ◽  
Vol 9 (1) ◽  
pp. 5376-5384
Keyword(s):  
Action Recognition ◽  
Video Retrieval ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Visual Surveillance ◽  
Benchmark Datasets ◽  
Active Research ◽  
The Individual ◽  
Individual Strategies

Human Action Recognition from videos has been an active research is in the computer vision due to its significant applicability in various real-time applications like video retrieval, human-robot interactions, and visual surveillance, etc. Though there are so many surveys over Human action Recognition, they are limited to various constraints like only focusing on the methods in few orientations only. Unlike the earlier ones, this paper provides a detailed survey according to the basic working methodology of Human action recognition system. Initially, a detailed illustration is given about various standard benchmark datasets. Further, following the methodology, the survey is accomplished in two phases, i.e., the survey over feature extraction approaches and the survey over action classification approaches. Further, a fine-grained survey is also accomplished under every phase based on the individual strategies

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 2D Information Space Based Action Recognition

2019 ◽  
Vol 9 (1S) ◽  
pp. 295-298
Keyword(s):  
Convex Hull ◽  
Action Recognition ◽  
Video Sequence ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
New Approach ◽  
Human Object ◽  
Background Data ◽  
2D Data

Video based human action recognition has attained more attraction from the researchers and it predominates in the field of computer vision and pattern recognition. In this paper we deliver a new approach to suppress the background data and to extract 2D data of foreground human object of the video sequence. A combination of convex hull area, convex hull perimeter, solidity and eccentricity is used to represent the feature vector. Experiments are conducted on Weizmann video dataset to assess how the system is doing. The discriminative nature of the feature vectors assures accuracy in action recognition.

Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines

2021 ◽  
pp. 1-22
Author(s):  
L. Nirmala Devi ◽  
A.Nageswar Rao
Keyword(s):  
Action Recognition ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Gabor Filter ◽  
Principal Component ◽  
Human Action Recognition ◽  
Human Action ◽  
Visual Surveillance ◽  
Support Vector ◽  
Significant Research

Human action recognition (HAR) is one of most significant research topics, and it has attracted the concentration of many researchers. Automatic HAR system is applied in several fields like visual surveillance, data retrieval, healthcare, etc. Based on this inspiration, in this chapter, the authors propose a new HAR model that considers an image as input and analyses and exposes the action present in it. Under the analysis phase, they implement two different feature extraction methods with the help of rotation invariant Gabor filter and edge adaptive wavelet filter. For every action image, a new vector called as composite feature vector is formulated and then subjected to dimensionality reduction through principal component analysis (PCA). Finally, the authors employ the most popular supervised machine learning algorithm (i.e., support vector machine [SVM]) for classification. Simulation is done over two standard datasets; they are KTH and Weizmann, and the performance is measured through an accuracy metric.

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%.

scholarly journals Human Action Recognition Based on Fusion Features Extraction of Adaptive Background Subtraction and Optical Flow Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shaoping Zhu ◽  
Limin Xia
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Background Subtraction ◽  
Flow Model ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
Multiple Instance Learning ◽  
Data Sets ◽  
Flow Feature

A novel method based on hybrid feature is proposed for human action recognition in video image sequences, which includes two stages of feature extraction and action recognition. Firstly, we use adaptive background subtraction algorithm to extract global silhouette feature and optical flow model to extract local optical flow feature. Then we combine global silhouette feature vector and local optical flow feature vector to form a hybrid feature vector. Secondly, in order to improve the recognition accuracy, we use an optimized Multiple Instance Learning algorithm to recognize human actions, in which an Iterative Querying Heuristic (IQH) optimization algorithm is used to train the Multiple Instance Learning model. We demonstrate that our hybrid feature-based action representation can effectively classify novel actions on two different data sets. Experiments show that our results are comparable to, and significantly better than, the results of two state-of-the-art approaches on these data sets, which meets the requirements of stable, reliable, high precision, and anti-interference ability and so forth.

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