An Adaptive Stacked Denoising Auto-Encoder Architecture for Human Action Recognition

2014 ◽  
Vol 631-632 ◽  
pp. 403-409 ◽  
Author(s):  
Dao Xi Wu ◽  
Wei Pan ◽  
Li Dong Xie ◽  
Chao Xi Huang
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Adaptive Learning ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Rate ◽  
Experimental Results ◽  
Classification Methods ◽  
Initial Value ◽  
Human Actions

In this paper, a stacked denoising auto-encoder architecture method with adaptive learning rate for action recognition based on skeleton features of human is presented. Firstly a Kinect is used for capturing the skeleton images and extracting skeleton features. Then an adaptive stacked denoising auto-encoder with three hidden layers is constructed for unsupervised pre-training. So the trained weights are achieved. Finally, a neural network is constructed for action recognition, in which the trained weights are used as the initial value, covering the random value. Based on the experimental results from the Kinect dataset of human actions sampled in experiments, it is clear to see that our method possesses the better robustness and accuracy, compared with the classic classification methods.

Research on Applied Technology in Human Action Recognition Based on Skeleton Information

2013 ◽  
Vol 859 ◽  
pp. 498-502 ◽  
Author(s):  
Zhi Qiang Wei ◽  
Ji An Wu ◽  
Xi Wang
Keyword(s):  
Data Analysis ◽  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Experimental Results ◽  
Human Actions ◽  
Applied Technology ◽  
Feature Sequence ◽  
Human Actions Recognition

In order to realize the identification of human daily actions, a method of identifying human daily actions is realized in this paper, which transforms this problem into converting human action recognition into analyzing feature sequence. Then the feature sequence combined with improved LCS algorithm could realize the human actions recognition. Data analysis and experimental results show the recognition rate of this method is high and speed is fast, and this applied technology will have broad prospects.

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%.

scholarly journals Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiulin Wang ◽  
Baole Tao ◽  
Fulei Han ◽  
Wenting Wei
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Human Actions ◽  
Basketball Game ◽  
Wide Range

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.

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