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

HLVU : A New Challenge to Test Deep Understanding of Movies the Way Humans do