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 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 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.

scholarly journals Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

2017 ◽  
Vol 7 (1.1) ◽  
pp. 489 ◽  
Author(s):  
P V.V. Kishore ◽  
P Siva Kameswari ◽  
K Niharika ◽  
M Tanuja ◽  
M Bindu ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Graph Kernels ◽  
Joint Distributions ◽  
Spatial Graph ◽  
3D Space ◽  
Machine Interface ◽  
Action Frame

Human action recognition is a vibrant area of research with multiple application areas in human machine interface. In this work, we propose a human action recognition based on spatial graph kernels on 3D skeletal data. Spatial joint features are extracted using joint distances between human joint distributions in 3D space. A spatial graph is constructed using 3D points as vertices and the computed joint distances as edges for each action frame in the video sequence. Spatial graph kernels between the training set and testing set are constructed to extract similarity between the two action sets. Two spatial graph kernels are constructed with vertex and edge data represented by joint positions and joint distances. To test the proposed method, we use 4 publicly available 3D skeletal datasets from G3D, MSR Action 3D, UT Kinect and NTU RGB+D. The proposed spatial graph kernels result in better classification accuracies compared to the state of the art models.

Recognition of Inter-Class Variation of Human Actions in Sports Video

2021 ◽  
Author(s):  
Akila.K
Keyword(s):  
Action Recognition ◽  
Wavelet Packet ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Human Actions ◽  
Linear Discriminant ◽  
Novel Approach ◽  
Current Events ◽  
Rule Set

Abstract Background: Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category.Objective: This paper focuses on a specific issue related to inter-class variation in Human Action Recognition.Approach: To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features are analyzed and discriminated by Deterministic Normalized - Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set..Conclusion: Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation

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