  In film production, metadata plays an important role in original raw video
indexing and classification within the industrial post-production software.
Inspired by deep visual-semantic methods, we propose an automated image
information extraction process to extend the diversity of metadata entities for
massive large-scale raw video searching and retrieval. In this paper, we
introduce the proposed system architecture and modules, integrating semantic
annotation models and user-demand-oriented information fusion. We conducted
experiments to validate the effectiveness of our system on Film Raw Video
Semantic Annotation Dataset (Film-RVSAD) and Slate Board Template Dataset
(SBTD), two benchmark datasets built for cinematography-related semantic
annotation and slate detection. Experimental results show that the proposed
system provides an effective strategy to improve the efficiency of metadata
generation and transformation, which is necessary and convenient for
collaborative work in the filmmaking process.
