Scalable Complex Event Processing on Video Streams

Abstract

The rapid expansion of video streaming content in our daily lives has rendered the real-time processing and analysis of these video streams a critical capability. However, existing deep video analytics systems primarily support only simple queries, such as selection and aggregation. Considering the inherent temporal nature of video streams, queries capable of matching patterns of events could enable a wider range of applications. In this paper, we present Bobsled, a novel video stream processing system designed to efficiently support complex event queries. Experimental results demonstrate that Bobsled can achieve a throughput improvement over state-of-the-art ranging from 2.4× to 11.6×, without any noticeable loss in accuracy.