Action Machine: Rethinking Action Recognition in Trimmed Videos

Understand

Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit the scenes and objects.

• In this work, we present a conceptually simple, general and high-performance framework for action recognition in trimmed videos, aiming at person-centric modeling.
• The method, called Action Machine, takes as inputs the videos cropped by person bounding boxes.
• It extends the Inflated 3D ConvNet (I3D) by adding a branch for human pose estimation and a 2D CNN for pose-based action recognition, being fast to train and test.