scholarly journals Algebra Based Human Skeleton Sequence Reduction and Action Recognition

2021 ◽  
Author(s):  
Shibin Xuan ◽  
Kuan Wang ◽  
Lixia Liu ◽  
Chang Liu ◽  
Jiaxiang Li
Keyword(s):  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Algebraic Method ◽  
Human Action ◽  
Video Frames ◽  
Virtual Classes ◽  
Skeleton Graph ◽  
Spatio Temporal ◽  
Correct Recognition Rate

Skeleton-based human action recognition is a research hotspot in recent years, but most of the research focuses on the spatio-temporal feature extraction by convolutional neural network. In order to improve the correct recognition rate of these models, this paper proposes three strategies: using algebraic method to reduce redundant video frames, adding auxiliary edges into the joint adjacency graph to improve the skeleton graph structure, and adding some virtual classes to disperse the error recognition rate. Experimental results on NTU-RGB-D60, NTU-RGB-D120 and Kinetics Skeleton 400 databases show that the proposed strategy can effectively improve the accuracy of the original algorithm.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Spatio-temporal SRU with global context-aware attention for 3D human action recognition

2020 ◽  
Vol 79 (17-18) ◽  
pp. 12349-12371
Author(s):  
Qingshan She ◽  
Gaoyuan Mu ◽  
Haitao Gan ◽  
Yingle Fan
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Context Aware ◽  
Global Context ◽  
Spatio Temporal

scholarly journals Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition

2020 ◽  
Vol 10 (12) ◽  
pp. 4412
Author(s):  
Ammar Mohsin Butt ◽  
Muhammad Haroon Yousaf ◽  
Fiza Murtaza ◽  
Saima Nazir ◽  
Serestina Viriri ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
Compact Representation ◽  
Agglomerative Clustering ◽  
Residual Vector ◽  
Benchmark Datasets ◽  
Codebook Generation ◽  
Spatio Temporal

Human action recognition has gathered significant attention in recent years due to its high demand in various application domains. In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos. The proposed scheme develops a discriminative codebook and a hybrid feature vector by encoding the features extracted from CNNs (convolutional neural networks). We explore different CNN architectures for extracting spatio-temporal features. We employ an agglomerative clustering approach for codebook generation, which intends to combine the advantages of global and class-specific codebooks. We propose a Residual Vector of Locally Aggregated Descriptors (R-VLAD) and fuse it with locality-based coding to form a hybrid feature vector. It provides a compact representation along with high order statistics. We evaluated our work on two publicly available standard benchmark datasets HMDB-51 and UCF-101. The proposed method achieves 72.6% and 96.2% on HMDB51 and UCF101, respectively. We conclude that the proposed scheme is able to boost recognition accuracy for human action recognition.

Research on Action Recognition of Human Body Based on Kinect Sensor

2014 ◽  
Vol 644-650 ◽  
pp. 4162-4166
Author(s):  
Dan Dan Guo ◽  
Xi’an Zhu
Keyword(s):  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Skeletal Structure ◽  
Depth Sensor ◽  
Depth Sensors ◽  
3D Space ◽  
3D Information

An effective Human action recognition method based on the human skeletal information which is extracted by Kinect depth sensor is proposed in this paper. Skeleton’s 3D space coordinates and the angles between nodes of human related actions are collected as action characteristics through the research of human skeletal structure, node data and research on human actions. First, 3D information of human skeletons is acquired by Kinect depth sensors and the cosine of relevant nodes is calculated. Then human skeletal information within the time prior to current state is stored in real time. Finally, the relevant locations of the skeleton nodes and the variation of the cosine of skeletal joints within a certain time are analyzed to recognize the human motion. This algorithm has higher adaptability and practicability because of the complicated sample trainings and recognizing processes of traditional method is not taken up. The results of the experiment indicate that this method is with high recognition rate.

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%.

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