scholarly journals Gymnastics Movement Signs Based on Network Communication and Body Contour Feature Extraction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haiyun Wang ◽  
Shujun Hu
Keyword(s):  
Feature Extraction ◽  
Action Recognition ◽  
Rapid Development ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Extraction Process ◽  
Network Communication ◽  
Body Contour ◽  
Contour Feature

With the rapid development of computer vision technology, human action recognition technology has occupied an important position in this field. The basic human action recognition system is mainly composed of three parts: moving target detection, feature extraction, and human action recognition. In order to understand the action signs of gymnastics, this article uses network communication and contour feature extraction to extract different morphological features during gymnastics. Then, the finite difference algorithm of edge curvature is used to classify different gymnastic actions and analyze and discuss the Gaussian background. A modular method, an improved hybrid Gaussian modeling method, is proposed, which adaptively selects the number of Gaussian distributions. The research results show that, compared with traditional contour extraction, the resolution of gymnastic motion features extracted through network communication and body contour features is clearer, and the increase rate is more than 30%. Moreover, the method proposed in this paper removes noise in the image extraction process, the effect is good, and the athlete’s action marks are very clear, which can achieve the research goal.

Image Based Human Action Recognition System to Support the Blind

2015 ◽  
Vol 42 (1) ◽  
pp. 138-143
Author(s):  
ByoungChul Ko ◽  
Mincheol Hwang ◽  
Jae-Yeal Nam
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action

VISUAL-BASED HUMAN ACTION RECOGNITION ON SMART PHONES BASED ON 2D AND 3D DESCRIPTORS

2012 ◽  
Vol 26 (08) ◽  
pp. 1260009 ◽  
Author(s):  
MARC BOSCH-JORGE ◽  
ANTONIO-JOSÉ SÁNCHEZ-SALMERÓN ◽  
CARLOS RICOLFE-VIALA
Keyword(s):  
Action Recognition ◽  
Human Subjects ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Smart Phones ◽  
Image Representations ◽  
And Performance ◽  
3D Descriptors

The aim of this work is to present a visual-based human action recognition system which is adapted to constrained embedded devices, such as smart phones. Basically, vision-based human action recognition is a combination of feature-tracking, descriptor-extraction and subsequent classification of image representations, with a color-based identification tool to distinguish between multiple human subjects. Simple descriptors sets were evaluated to optimize recognition rate and performance and two dimensional (2D) descriptors were found to be effective. These sets installed on the latest phones can recognize human actions in videos in less than one second with a success rate of over 82%.

HUMAN ACTION RECOGNITION USING META-COGNITIVE NEURO-FUZZY INFERENCE SYSTEM

2012 ◽  
Vol 22 (06) ◽  
pp. 1250028 ◽  
Author(s):  
K. SUBRAMANIAN ◽  
S. SURESH
Keyword(s):  
Action Recognition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Sequential Learning ◽  
Inference System ◽  
Neuro Fuzzy

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.

scholarly journals A Comprehensive Survey on Human Action Recognition

2020 ◽  
Vol 9 (2) ◽  
pp. 902-908
Keyword(s):  
Feature Extraction ◽  
Action Recognition ◽  
Human Activity ◽  
Human Action Recognition ◽  
Present Situation ◽  
Human Action ◽  
Multiple Parameters ◽  
Multiple Feature ◽  
Comprehensive Survey ◽  
Complete Process

The present The present situation is having many challenges in security and surveillance of Human Action recognition (HAR). HAR has many fields and many techniques to provide modern and technical action implementation. We have studied multiple parameters and techniques used in HAR. We have come out with a list of outcomes and drawbacks of each technique present in different researches. This paper presents the survey on the complete process of recognition of human activity and provides survey on different Motion History Imaging (MHI) methods, model based, multiview and multiple feature extraction based recognition methods.

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