Paper Info
Title
An In-Database Streaming Solution to Multi-camera Fusion
Abstract
Multi-camera based video object tracking is a multi-stream data fusion and analysis problem. With the current technology, video analysis software architecture generally separates the analytics layer from the data management layer, which has become the performance bottleneck because of large scaled data transfer, inefficient data access and duplicate data buffering and management. Motivated by providing a convergent platform, we use user-defined Relation Valued Functions (RVFs) to have visual data computation naturally integrated to SQL queries, and pushed down to the database engine; we model complex applications with general graph based data-flows and control-flows at the process level where "actions" are performed by RVFs and "linked" in SQL queries. We further introduce Stream Query Process with stream data input and continuous execution. Our solutions to multi-camera video surveillance also include a new tracking method that is based on P2P time-synchronization of video streams and P2P target fusion. These techniques represent a major shift in process management from one-time execution to data stream driven, open-ended execution, and constitute a novel step to the use of a query engine for running processes, towards the "In-DB Streaming" paradigm. We have prototyped the proposed approaches by extending the open-sourced database engine Postgres, and plan to transfer the implementation to a commercial and proprietary parallel database system. The empirical study in a surveillance setting reveals their advantages in scalability, real-time performance and simplicity.
Year
DOI
Venue
2009
10.1007/978-3-642-03715-3_12
Globe
Keywords
Field
DocType
visual data,data management layer,multi-camera fusion,in-database streaming solution,stream data input,duplicate data,sql query,multi-stream data fusion,inefficient data access,video object tracking,video analysis software architecture,data stream,data fusion,data management,control flow,software architecture,data access,real time,data transfer,process management,empirical study,value function,data flow,p2p
SQL,Data stream,Computer science,Parallel database,Real-time computing,Video tracking,Database engine,Data management,Data access,Database,Scalability
Conference
Volume
ISSN
Citations 
5697
0302-9743
2
PageRank 
References 
Authors
0.42
12
4
Name
Order
Citations
PageRank
Qiming Chen12010233.16
Qinghu Li2122.75
Meichun Hsu33437778.34
Tao Yu450.83