Action recognition algorithm based on skeletal joint data and adaptive time pyramid

2022 ◽  
Author(s):  
Mingjun Sima ◽  
Mingzheng Hou ◽  
Xin Zhang ◽  
Jianwei Ding ◽  
Ziliang Feng
Keyword(s):  
Action Recognition ◽  
Recognition Algorithm ◽  
Adaptive Time

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.

scholarly journals I3D-Shufflenet Based Human Action Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 301
Author(s):  
Guocheng Liu ◽  
Caixia Zhang ◽  
Qingyang Xu ◽  
Ruoshi Cheng ◽  
Yong Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Convolution Kernel ◽  
Histogram Of Oriented Gradients ◽  
Temporal Features ◽  
Convolution Kernels

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.

Mixed Features Based Improved Human Action Recognition Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2731-2734
Author(s):  
Hai Long Jia ◽  
Kun Cao
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Video Camera ◽  
Human Action ◽  
Recognition Algorithm ◽  
Advantages And Disadvantages ◽  
Single Feature ◽  
Mixed Features ◽  
Motion Features ◽  
Full Consideration

The choice of the motion features affects the result of the human action recognition method directly. Many factors often influence the single feature differently, such as appearance of human body, environment and video camera. So the accuracy of action recognition is limited. On the basis of studying the representation and recognition of human actions, and giving full consideration to the advantages and disadvantages of different features, this paper proposes a mixed feature which combines global silhouette feature and local optical flow feature. This combined representation is used for human action recognition.

Human Action Recognition Algorithm Based on Key Posture

2013 ◽  
Vol 631-632 ◽  
pp. 1303-1308
Author(s):  
He Jin Yuan
Keyword(s):  
Action Recognition ◽  
Clustering Algorithm ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Training Samples ◽  
Action Sequences

A novel human action recognition algorithm based on key posture is proposed in this paper. In the method, the mesh features of each image in human action sequences are firstly calculated; then the key postures of the human mesh features are generated through k-medoids clustering algorithm; and the motion sequences are thus represented as vectors of key postures. The component of the vector is the occurrence number of the corresponding posture included in the action. For human action recognition, the observed action is firstly changed into key posture vector; then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can exceed 90%.

scholarly journals Action Recognition for the Robotics and Manufacturing Automation Using 3-D Binary Micro-block Difference

2021 ◽  
Author(s):  
Viacheslav Voronin ◽  
Marina Zhdanova ◽  
Evgenii Semenishchev ◽  
Aleksander Zelenskii ◽  
Yigang Cen ◽  
...  
Keyword(s):  
Control Systems ◽  
Action Recognition ◽  
Recognition Algorithm ◽  
Working Environment ◽  
Visible Range ◽  
Data Sets ◽  
Effective Algorithm ◽  
Manufacturing Automation ◽  
Depth Sensors ◽  
Multimodal Information

Abstract Vision-based control systems play an important role in modern robotics systems. An important task in implementing such a system is the development of an effective algorithm for recognizing human actions and the working environment and the design of intuitive gesture commands. This paper proposes an action recognition algorithm for robotics and manufacturing automation. The key contributions are (1) fusion of multimodal information obtained by depth sensors and cameras of the visible range, (2) modified Gabor-based and 3-D binary-based descriptor using micro-block difference, (3) efficient skeleton-based descriptor, and (4) recognition algorithm using the combined descriptor. The proposed binary micro-block difference representation of 3-D patches from video with a complex background in several scales and orientations leads to an informative description of the scene action. The experimental results showed the effectiveness of the proposed algorithm on data sets.

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