Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN

Understand

Learning the spatial-temporal representation of motion information is crucial to human action recognition.

• Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.
• To address this problem, this paper proposes a long-term motion descriptor called sequential Deep Trajectory Descriptor (sDTD).
• Specifically, we project dense trajectories into two-dimensional planes, and subsequently a CNN-RNN network is employed to learn an effective representation for long-term motion.