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Complex Event Processing (CEP) is an event processing paradigm to performreal-time analytics over streaming data and match high-level event patterns.Presently, CEP is limited to process structured data stream. Video streams arecomplicated due to their unstructured data model and limit CEP systems toperform matching over them. This work introduces a graph-based structure forcontinuous evolving video streams, which enables the CEP system to querycomplex video event patterns. We propose the Video Event Knowledge Graph(VEKG), a graph driven representation of video data. VEKG models video objectsas nodes and their relationship interaction as edges over time and space. Itcreates a semantic knowledge representation of video data derived from thedetection of high-level semantic concepts from the video using an ensemble ofdeep learning models. A CEP-based state optimization - VEKG-Time AggregatedGraph (VEKG-TAG) is proposed over VEKG representation for faster eventdetection. VEKG-TAG is a spatiotemporal graph aggregation method that providesa summarized view of the VEKG graph over a given time length. We defined a setof nine event pattern rules for two domains (Activity Recognition and TrafficManagement), which act as a query and applied over VEKG graphs to discovercomplex event patterns. To show the efficacy of our approach, we performedextensive experiments over 801 video clips across 10 datasets. The proposedVEKG approach was compared with other state-of-the-art methods and was able todetect complex event patterns over videos with F-Score ranging from 0.44 to0.90. In the given experiments, the optimized VEKG-TAG was able to reduce 99%and 93% of VEKG nodes and edges, respectively, with 5.19X faster search time,achieving sub-second median latency of 4-20 milliseconds.

Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing