  Human action recognition (HAR) is a high-level and significant research area
in computer vision due to its ubiquitous applications. The main limitations of
the current HAR models are their complex structures and lengthy training time.
In this paper, we propose a simple yet versatile and effective end-to-end deep
learning architecture, coined as TransNet, for HAR. TransNet decomposes the
complex 3D-CNNs into 2D- and 1D-CNNs, where the 2D- and 1D-CNN components
extract spatial features and temporal patterns in videos, respectively.
Benefiting from its concise architecture, TransNet is ideally compatible with
any pretrained state-of-the-art 2D-CNN models in other fields, being
transferred to serve the HAR task. In other words, it naturally leverages the
power and success of transfer learning for HAR, bringing huge advantages in
terms of efficiency and effectiveness. Extensive experimental results and the
comparison with the state-of-the-art models demonstrate the superior
performance of the proposed TransNet in HAR in terms of flexibility, model
complexity, training speed and classification accuracy.
