The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition

Understand

Motivated by the success of data-driven convolutional neural networks (CNNs) in object recognition on static images, researchers are working hard towards developing CNN equivalents for learning video features.

• However, learning video features globally has proven to be quite a challenge due to its high dimensionality, the lack of labelled data and the difficulty in processing large-scale video data.
• Therefore, we propose to leverage effective techniques from both data-driven and data-independent approaches to improve action recognition system.
• Our contribution is three-fold.