  Human action recognition has become one of the most active field of research
in computer vision due to its wide range of applications, like surveillance,
medical, industrial environments, smart homes, among others. Recently, deep
learning has been successfully used to learn powerful and interpretable
features for recognizing human actions in videos. Most of the existing deep
learning approaches have been designed for processing video information as RGB
image sequences. For this reason, a preliminary decoding process is required,
since video data are often stored in a compressed format. However, a high
computational load and memory usage is demanded for decoding a video. To
overcome this problem, we propose a deep neural network capable of learning
straight from compressed video. Our approach was evaluated on two public
benchmarks, the UCF-101 and HMDB-51 datasets, demonstrating comparable
recognition performance to the state-of-the-art methods, with the advantage of
running up to 2 times faster in terms of inference speed.
