VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching

Abstract

Complex Event Processing (CEP) is a paradigm to detect event patterns over streaming data in a timely manner. Presently, CEP systems have inherent limitations to detect event patterns over video streams due to their data complexity and lack of structured data model. Modelling complex events in unstructured data like videos not only requires detecting objects but also the spatiotemporal relationships among objects. This work introduces a novel video representation technique where an input video stream is converted to a stream of graphs. We propose the Video Event Knowledge Graph (VEKG), a knowledge graph driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. To optimize the run-time system performance, we introduce a graph aggregation method VEKG-TAG, which provides an aggregated view of VEKG for a given time length. We defined a set of operators using event rules which can be used as a query and applied over VEKG graphs to discover complex video patterns. The system achieves an F-Score accuracy ranging between 0.75 to 0.86 for different patterns when queried over VEKG. In given experiments, pattern search time over VEKG-TAG was 2.3X faster as compared to the baseline.