Scaling Up the Training of Deep CNNs for Human Action Recognition

VN Balasubramanian
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks such as object recognition. They are gaining huge importance in recent times but are computationally intensive. Typically trained on massive datasets, two-dimensional CNNs are used for image classification and recognition purposes and consume huge computational time. For applications like human action recognition involving video inputs, their 3D counterparts termed as 3D convolutional neural networks (3D-CNNs) are ...