  Process mining is one of the most active research streams in business process
management. In recent years, numerous methods have been proposed for analyzing
structured process data. Yet, in many cases, it is only the digitized parts of
processes that are directly captured from process-aware information systems,
and manual activities often result in blind spots. While the use of video
cameras to observe these activities could help to fill this gap, a standardized
approach to extracting event logs from unstructured video data remains lacking.
Here, we propose a reference architecture to bridge the gap between computer
vision and process mining. Various evaluation activities (i.e., competing
artifact analysis, prototyping, and real-world application) ensured that the
proposed reference architecture allows flexible, use-case-driven, and
context-specific instantiations. Our results also show that an exemplary
software prototype instantiation of the proposed reference architecture is
capable of automatically extracting most of the process-relevant events from
unstructured video data.
