Multimedia fingerprinting (robust hashing) as a content identification technology is emerging as an effective tool
for preventing unauthorized distribution of commercial content through user generated content (UGC) sites.
Research in the field has mainly considered content types with slow distribution cycles, e.g. feature films, for
which reference fingerprint ingestion and database indexing can be performed offline. As a result, research focus
has been on improving the robustness and search speed.
Live events, such as live sports broadcasts, impose new challenges on a fingerprinting system. For instance,
highlights from a soccer match are often available-and viewed-on UGC sites well before the end of the match.
In this scenario, the fingerprinting system should be able to ingest and index live content online and offer
continuous search capability, where new material is identifiable within minutes of broadcast. In this paper, we
concentrate on algorithmic and architectural challenges we faced when developing a video fingerprinting solution
for live events. In particular, we discuss how to effectively utilize fast sorting algorithms and a master-slave
architecture for fast and continuous ingestion of live broadcasts.