Human action recognition using support vector machines and 3D convolutional neural networks
Human action recognition using support vector machines and 3D convolutional neural networks
Price : 12000
.svg)
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.
Proposed System
In this study, we use 3D CNNs in order to extract features from stacked video frames. First one
uses stacked frames as input whereas the second one uses the dense optical flow component (Table
1) between two consecutive frames using Farneback algorithm [44].
Table 1. Different actions with each correspondence dense optical flow
Proposed System
In this study, we use 3D CNNs in order to extract features from stacked video frames. First one
uses stacked frames as input whereas the second one uses the dense optical flow component (Table
1) between two consecutive frames using Farneback algorithm [44].
Table 1. Different actions with each correspondence den