scholarly journals Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

2016 ◽  
Vol 6 (10) ◽  
pp. 309 ◽  
Author(s):  
Allah Sargano ◽  
Plamen Angelov ◽  
Zulfiqar Habib
Keyword(s):  
Support Vector Machines ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Feature Descriptor ◽  
Multiple Views ◽  
Invariant Feature ◽  
Vector Machines

scholarly journals Human action recognition using support vector machines and 3D convolutional neural networks

2017 ◽  
Vol 3 (1) ◽  
pp. 47 ◽  
Author(s):  
Majd Latah
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Recognition Task ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Vector Machines

Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.

scholarly journals Feature Fusion of Deep Spatial Features and Handcrafted Spatiotemporal Features for Human Action Recognition

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1599 ◽  
Author(s):  
Md Uddin ◽  
Young-Koo Lee
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Feature Descriptor ◽  
Weber’S Law ◽  
Weber's Law ◽  
Spatiotemporal Features ◽  
Spatial Features

Human action recognition plays a significant part in the research community due to its emerging applications. A variety of approaches have been proposed to resolve this problem, however, several issues still need to be addressed. In action recognition, effectively extracting and aggregating the spatial-temporal information plays a vital role to describe a video. In this research, we propose a novel approach to recognize human actions by considering both deep spatial features and handcrafted spatiotemporal features. Firstly, we extract the deep spatial features by employing a state-of-the-art deep convolutional network, namely Inception-Resnet-v2. Secondly, we introduce a novel handcrafted feature descriptor, namely Weber’s law based Volume Local Gradient Ternary Pattern (WVLGTP), which brings out the spatiotemporal features. It also considers the shape information by using gradient operation. Furthermore, Weber’s law based threshold value and the ternary pattern based on an adaptive local threshold is presented to effectively handle the noisy center pixel value. Besides, a multi-resolution approach for WVLGTP based on an averaging scheme is also presented. Afterward, both these extracted features are concatenated and feed to the Support Vector Machine to perform the classification. Lastly, the extensive experimental analysis shows that our proposed method outperforms state-of-the-art approaches in terms of accuracy.

scholarly journals A novel feature fusion based Human Action Recognition in 2D Videos

2018 ◽  
Vol 7 (2.20) ◽  
pp. 207 ◽  
Author(s):  
K Rajendra Prasad ◽  
P Srinivasa Rao
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Human Actions

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.  

RECOGNITION OF BASIC HUMAN ACTIONS USING DEPTH INFORMATION

2014 ◽  
Vol 28 (02) ◽  
pp. 1450004 ◽  
Author(s):  
ALI SEYDI KEÇELI ◽  
AHMET BURAK CAN
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Depth Information ◽  
Human Actions ◽  
Depth Sensors ◽  
Vector Machines ◽  
3D Environments ◽  
Game Console

Human action recognition using depth sensors is an emerging technology especially in game console industry. Depth information can provide robust features about 3D environments and increase accuracy of action recognition in short ranges. This paper presents an approach to recognize basic human actions using depth information obtained from the Kinect sensor. To recognize actions, features extracted from angle and displacement information of joints are used. Actions are classified using support vector machines and random forest (RF) algorithm. The model is tested on HUN-3D, MSRC-12, and MSR Action 3D datasets with various testing approaches and obtained promising results especially with the RF algorithm. The proposed approach produces robust results independent from the dataset with simple and computationally cheap features.

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.

A Set of New Hermite Kernel Functions in Kernel Extreme Learning Machine and Application in Human Action Recognition

2019 ◽  
Vol 33 (12) ◽  
pp. 1955014 ◽  
Author(s):  
Xueping Liu ◽  
Xingzuo Yue
Keyword(s):  
Extreme Learning Machine ◽  
Action Recognition ◽  
Structural Information ◽  
Image Data ◽  
Human Action Recognition ◽  
Human Action ◽  
Kernel Functions ◽  
Support Vector ◽  
Learning Speed ◽  
Learning Machine

The kernel function has been successfully utilized in the extreme learning machine (ELM) that provides a stabilized and generalized performance and greatly reduces the computational complexity. However, the selection and optimization of the parameters constituting the most common kernel functions are tedious and time-consuming. In this study, a set of new Hermit kernel functions derived from the generalized Hermit polynomials has been proposed. The significant contributions of the proposed kernel include only one parameter selected from a small set of natural numbers; thus, the parameter optimization is greatly facilitated and excessive structural information of the sample data is retained. Consequently, the new kernel functions can be used as optimal alternatives to other common kernel functions for ELM at a rapid learning speed. The experimental results showed that the proposed kernel ELM method tends to have similar or better robustness and generalized performance at a faster learning speed than the other common kernel ELM and support vector machine methods. Consequently, when applied to human action recognition by depth video sequence, the method also achieves excellent performance, demonstrating its time-based advantage on the video image data.

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