  In this paper we propose a new evaluation challenge and direction in the area
of High-level Video Understanding. The challenge we are proposing is designed
to test automatic video analysis and understanding, and how accurately systems
can comprehend a movie in terms of actors, entities, events and their
relationship to each other. A pilot High-Level Video Understanding (HLVU)
dataset of open source movies were collected for human assessors to build a
knowledge graph representing each of them. A set of queries will be derived
from the knowledge graph to test systems on retrieving relationships among
actors, as well as reasoning and retrieving non-visual concepts. The objective
is to benchmark if a computer system can "understand" non-explicit but obvious
relationships the same way humans do when they watch the same movies. This is
long-standing problem that is being addressed in the text domain and this
project moves similar research to the video domain. Work of this nature is
foundational to future video analytics and video understanding technologies.
This work can be of interest to streaming services and broadcasters hoping to
provide more intuitive ways for their customers to interact with and consume
video content.
