Efficient object feature selection for action recognition
Currently most action recognition or video classification tasks highly rely on the motion features such as state-of-the-art Improved Dense Trajectory (IDT) features. Despite the huge success, IDT features lack of rich static object-level information. In this paper, we make use of the object-level features for action recognition tasks. For efficiently and effectively processing large-scale video data, we propose a two-layer feature selection framework including local object feature selection (LS) and global feature selection (GS). Both of the selection methods can improve recognition accuracy while greatly reducing the feature dimension or feature processing complexity. Experimental results show that the selected object-level features contain complimentary information to IDT features and the combination with IDT features can further improve the recognition accuracy significantly.