Evaluation of Triple-Stream Convolutional Networks for Action Recognition

Recently, Two-Stream Convolutional Network has achieved remarkable performance. Especially, by capturing appearance and motion information, spatial-temporal two- stream networks bring noticeable improvement. On the other hand, dynamic image, which is a powerful representation for videos, has also been confirmed to provide complimentary information to spatial appearance. Inspired by these works, we proposed Triple-Stream Convolutional Networks by fusing a third network stream whose input is dynamic image. In this paper, we implement the proposed Triple-Stream Convolutional Networks and evaluated them in two aspects: (a) how the overall end-to-end classification performance can be benefited by adding the dynamic stream; (b) which way is efficient to use the trained Triple-Stream Convolutional Networks in classification. Our evaluation shows improvements over both single networks (spatial and temporal) and Fused Spatial-temporal Two-Stream Network.