Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition

Understand

Action recognition is an important research topic in computer vision.

• It is the basic work for visual understanding and has been applied in many fields.
• Since human actions can vary in different environments, it is difficult to infer actions in completely different states with a same structural model.
• For this case, we propose a Cross-Enhancement Transform Two-Stream 3D ConvNets algorithm, which considers the action distribution characteristics on the specific dataset.