Cameras are ubiquitous nowadays and video analytic systems have been widely used in surveillance, traffic control, business intelligence and autonomous driving. Some applications, e.g., detecting road congestion in traffic monitoring, require continuous and timely reporting of complex patterns. However, conventional complex event processing (CEP) systems fail to support video processing, while the existing video query languages offer limited support for expressing advanced CEP queries, such as iteration, and window. In this PhD research, we aim to develop systems and methods to alleviate these issues. In this paper, we first identify the need for an expressive CEP language which allows users to define queries over video streams, and receive fast, accurate results. To evaluate CEP queries on videos in real-time and with high accuracy, we explain how a streaming query engine can be designed to provide native support of machine learning (ML) models for fast and accurate inference on video streams. In addition, we describe a set of optimization problems that arise when ML models, with trade-offs in speed, accuracy, and cost, are part of a query plan. Finally, we describe how query plans on real-time videos can be optimized and deployed on edge devices with limited computational and network capabilities.
|Number of pages||4|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2020|
|Event||2020 International Conference on Very Large Databases PhD Workshop, VLDB-PhD 2020 - Virtual, Online, Japan|
Duration: 31 Aug 2020 → 4 Sep 2020