Open AccessHuman Action Recognition Using A Novel Deep Learning Approach
Author(s) -
Badhagouni Suresh Kumar,
S. Viswanadha Raju,
H. Venkateswara Reddy
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1042/1/012031
Subject(s) - computer science , action (physics) , artificial intelligence , action recognition , computation , feature extraction , artificial neural network , pattern recognition (psychology) , feature (linguistics) , machine learning , set (abstract data type) , data set , activity recognition , algorithm , class (philosophy) , linguistics , philosophy , physics , quantum mechanics , programming language
Behavior human analysis is always a significant aspect in societal communication. The human behavior analysis is developed based on few factors like human activity and action recognition. Human action recognition is an significant feature in different safety fields. The assessment of the action recognition algorithm depends on the appropriate extraction and the learning data. In the human action recognition, classification plays the major role so in order to this effectively Gated Recurrent Neural Network is used with an increased computation level. Feature extraction is one of the essential factor in human action recognition it will influence the performance and computation time of the algorithm. This paper presented an approach for human action recognition based on new mixture deep learning model. The proposed method is evaluated on the different data sets like UCF Sports, KTH and UCF101. On UCF Sports data set the proposed method has given an average of 96.8%.