DAP3D-Net: Where, What and How Actions Occur in Videos?

Understand

Action parsing in videos with complex scenes is an interesting but challenging task in computer vision.

• In this paper, we propose a generic 3D convolutional neural network in a multi-task learning manner for effective Deep Action Parsing (DAP3D-Net) in videos.
• Particularly, in the training phase, action localization, classification and attributes learning can be jointly optimized on our appearancemotion data via DAP3D-Net.
• For an upcoming test video, we can describe each individual action in the video simultaneously as: Where the action occurs, What the action is and How the action is performed.