Human action recognition using support vector machines and 3D convolutional neural networks
Articles > Article ยท 2017
Authors: Majd Latah
International Journal of Advances in Intelligent Informatics, International, Scopus, 2017, vol 3, pp 47-47
Summary
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.