  With the advancement in computer vision deep learning, systems now are able
to analyze an unprecedented amount of rich visual information from videos to
enable applications such as autonomous driving, socially-aware robot assistant
and public safety monitoring. Deciphering human behaviors to predict their
future paths/trajectories and what they would do from videos is important in
these applications. However, human trajectory prediction still remains a
challenging task, as scene semantics and human intent are difficult to model.
Many systems do not provide high-level semantic attributes to reason about
pedestrian future. This design hinders prediction performance in video data
from diverse domains and unseen scenarios. To enable optimal future human
behavioral forecasting, it is crucial for the system to be able to detect and
analyze human activities as well as scene semantics, passing informative
features to the subsequent prediction module for context understanding.
