  With the recent substantial growth of media such as YouTube, a considerable
number of instructional videos covering a wide variety of tasks are available
online. Therefore, online instructional videos have become a rich resource for
humans to learn everyday skills. In order to improve the effectiveness of the
learning with instructional video, observation and evaluation of the activity
are required. However, it is difficult to observe and evaluate every activity
steps by expert. In this study, a novel deep learning framework which targets
human activity evaluation for learning from instructional video has been
proposed. In order to deal with the inherent variability of activities, we
propose to model activity as a structured process. First, action units are
encoded from dense trajectories with LSTM network. The variable-length action
unit features are then evaluated by a Siamese LSTM network. By the comparative
experiments on public dataset, the effectiveness of the proposed method has
been demonstrated.
