Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition

Understand

Convolutional neural networks with spatio-temporal 3D kernels (3D CNNs) have an ability to directly extract spatio-temporal features from videos for action recognition.

• Although the 3D kernels tend to overfit because of a large number of their parameters, the 3D CNNs are greatly improved by using recent huge video databases.
• However, the architecture of 3D CNNs is relatively shallow against to the success of very deep neural networks in 2D-based CNNs, such as residual networks (ResNets).
• In this paper, we propose a 3D CNNs based on ResNets toward a better action representation.