Open AccessHuman action recognition using support vector machines and 3D convolutional neural networks
Author(s) -
Majd Latah
Publication year - 2017
Publication title -
ijain (international journal of advances in intelligent informatics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.183
H-Index - 9
eISSN - 2548-3161
pISSN - 2442-6571
DOI - 10.26555/ijain.v3i1.89
Subject(s) - computer science , convolutional neural network , artificial intelligence , support vector machine , pattern recognition (psychology) , action recognition , task (project management) , action (physics) , deep learning , machine learning , management , economics , class (philosophy) , physics , quantum mechanics
Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.