A Data Cube Model for Surveillance Video Indexing and Retrieval
Hansung Lee, Sohee Park, Jang‐Hee Yoo · 2013
We propose a novel data cube model, viz., SurvCube, for the multi-dimensional indexing and retrieval of surveillance videos. The proposed method provides the multi-dimensional analysis of interesting objects in surveillance videos according to the chronological view, events and locations by means of data cube structure. By employing the OLAP operation on the surveillance videos, it is able to provides desirable functionalities such as 1) retrieval of objects and events at a different level of abstraction, i.e., coarse to fine grained retrieval; 2) providing the tracing of interesting object trajectories across the cameras; 3) providing the summarization of surveillance video with respect to interesting objects (and/or events) and abstract level of time and locations.