Multi-Camera Video Scene Graphs for Surveillance Videos Indexing and Retrieval

Abstract

Modern video surveillance systems often consist of multiple cameras capturing continuous videos. However, existing systems do not fully take advantage of shared scene semantics across multiple video streams, and hence querying the captured videos to find an event or object of interest can be a time-consuming task. In this paper, we propose a compact representation of objects and their relationships in the scene under surveillance, over time, and across multiple cameras, using a combined global spatio-temporal scene graph. The same objects that appear in multiple cameras are stored only once, reducing the storage required and speeding up the query when compared to storing the scene graph from each camera individually. Our experiments show that in a 5-camera system our proposed representation can speed up querying time by a factor of 3.9 times.

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

“…Geotagged video is natural perception data that contains rich spatial semantics (Han et al, 2016; Jamonnak et al, 2021; Lewis et al, 2011). Integrating surveillance video data with a geographic information system (GIS), using a unified geographic reference system for video data management and analysis, and enhancing urban video surveillance systems are very useful for urban security (Milosavljević et al, 2016; Patel et al, 2021; Wu et al, 2017; Xie et al, 2017). Continual progress in object detection and tracking algorithms based on deep learning has enabled the implementation of intelligent analysis and target perception of surveillance videos by extracting moving objects from surveillance videos.…”