scholarly journals Human Action Recognition Using A Multi-Modal Hybrid Deep Learning Model

2017 ◽  
Author(s):  
Hany El-Ghaish ◽  
Mohamed Hussein ◽  
Amin Shoukry
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Model ◽  
Deep Learning Model

Multiple stream deep learning model for human action recognition

2020 ◽  
Vol 93 ◽  
pp. 103818 ◽  
Author(s):  
Ye Gu ◽  
Xiaofeng Ye ◽  
Weihua Sheng ◽  
Yongsheng Ou ◽  
Yongqiang Li
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Model ◽  
Deep Learning Model

Human Action Recognition Based on Multiple Features and Modified Deep Learning Model

2020 ◽  
Vol 34 (10) ◽  
pp. 2055022 ◽  
Author(s):  
Shaoping Zhu ◽  
Yongliang Xiao ◽  
Weimin Ma
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Large Data ◽  
Human Action Recognition ◽  
Human Action ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Multiple Features ◽  
Deep Learning Model

In order to improve the accuracy of human action recognition in video and the computational efficiency of large data sets, an action recognition algorithm based on multiple features and modified deep learning model is proposed. First, the deep network pre-training process is used to learn and optimize the RBM parameters, and the deep belief nets (DBN) model is constructed through deep learning. Then, human 13 joint points and critical points of optical flow are automatically extracted by DBN model. Second, these more abstract and more effective human motion features are combined to represent human actions. Ultimately, the entire DBN network structure is fine-tuned by support vector machine (SVM) algorithm to classify human actions. We demonstrate that human 13 joint points and critical points of optical flow are two very effective human action characterizations, our proposed approach greatly reduces the required samples, and shortens the training time of the samples, can efficiently process large data sets and can effectively recognize novel actions. We performed experiments on the KTH data set, Weizmann data set, the ballet data set and UCF101 data set to evaluate the proposed method, the experiment results show that the average recognition accuracy is over 98%, which validates its effectiveness, and show that our results are stable, reliable, and significantly better than the results of two state-of-the-art approaches on four different data sets. So, it lays a good theoretical foundation for practical applications.

scholarly journals Child Activity Recognition using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 364-367
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Health Monitoring ◽  
Human Action Recognition ◽  
Human Action ◽  
Supervision System ◽  
Learning Techniques ◽  
Potential Applications ◽  
The Subject ◽  
Deep Learning Model

The human action recognition is the subject to predicting what an individual is performing based on a trace of their development exploiting a several strategies. Perceiving human activities is an ordinary region of eagerness in view of its various potential applications; though, it is still in start. It is a trending analysis area possessed by the range from dependable automation, medicinal services to developing the smart supervision system. In this work, we are trying to recognize the activity of the child from video dataset using deep learning techniques. The proposed system will help parent to take care of their baby during the job or from anywhere else to know what the baby is doing. This can also be useful to prevent the in-house accident falls of the child and for health monitoring. The activities can be performed by child include sleeping, walking, running, crawling, playing, eating, cruising, clapping, laughing, crying and many more. We are focusing on recognizing crawling, running, sleeping, and walking activities of the child in this study. The offered system gives the best result compared with the existing methods, which utilize sensor-based information. Experimental results proved that the offered deep learning model had accomplished 94.73% accuracy for recognizing the child activity.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
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