Online Localization and Prediction of Actions and Interactions

Understand

This paper proposes a person-centric and online approach to the challenging problem of localization and prediction of actions and interactions in videos.

• Typically, localization or recognition is performed in an offline manner where all the frames in the video are processed together.
• This prevents timely localization and prediction of actions and interactions - an important consideration for many tasks including surveillance and human-machine interaction.
• In our approach, we estimate human poses at each frame and train discriminative appearance models using the superpixels inside the pose bounding boxes.