scholarly journals Robust Feature-Based Automated Multi Viewhuman Action Recognition System Using Machine Learning

2019 ◽  
pp. 52-58
Author(s):  
Paruchuri Yogesh ◽  
R. Dillibabu ◽  
Palaniappan P
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture Model ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Human Action ◽  
Public Security ◽  
Multiview Video ◽  
Feature Based

Automated human action Recognition has the potential to play an important role in Public security. In this project it compares three practical, reliable and generics systems for multiview video based human action recognition namely the nearest classifier, Gaussian mixture model classifier and nearest mean classifier.

scholarly journals Robust Feature-Based Automated Multi-View Human Action Recognition System

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 15283-15296 ◽  
Author(s):  
Kuang-Pen Chou ◽  
Mukesh Prasad ◽  
Di Wu ◽  
Nabin Sharma ◽  
Dong-Lin Li ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Feature Based

scholarly journals Human action recognition based on mixed gaussian hidden markov model

2021 ◽  
Vol 336 ◽  
pp. 06004
Author(s):  
Jiawei Xu ◽  
Qian Luo
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Action Recognition ◽  
Hidden Markov ◽  
Human Action Recognition ◽  
Gaussian Mixture ◽  
Human Action ◽  
Model Based

Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.

scholarly journals Study on Machine Learning and Deep Learning Methods for Human Action Recognition

2020 ◽  
Author(s):  
Gopika Rajendran ◽  
Ojus Thomas Lee ◽  
Arya Gopi ◽  
Jais jose ◽  
Neha Gautham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Action Recognition ◽  
Human Body ◽  
Human Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Robot Interaction ◽  
Learning Approach ◽  
Action Sequence

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

scholarly journals Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Chao Tang ◽  
Huosheng Hu ◽  
Wenjian Wang ◽  
Wei Li ◽  
Hua Peng ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Image Features ◽  
Depth Image ◽  
Good Ability ◽  
Multimodal Features ◽  
Feature Based

The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

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.

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