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A Data Cube Model for Surveillance Video Indexing and Retrieval
Hansung Lee, Sohee Park
and Jang-Hee Yoo
Electronics and Telecommunications Research Institute, Daejeon, Korea
Keywords: Data Cube, Surveillance Video, OLAP, Video Indexing, Video Retrieval.
Abstract: 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.
1 INTRODUCTION
The CCTV video surveillance system has been
developed for the public and private security, and
safety. The main purposes of the CCTV surveillance
systems are real-time monitoring of the interesting
areas and supporting criminal investigation at initial
stage. The CCTV cameras at the most public areas
are working and recording a huge numbers of
surveillance videos for the criminal prevention and
investigation. With the recent exploding of
surveillance videos, it is more difficult to find
meaningful information in manual way from large
data collections. Therefore, the surveillance video
databases have extensively studied for over past
decade to provide indexing, browsing, retrieval and
analysis of surveillance videos.
The conventional surveillance video database
systems, which are developed as a part of the video
surveillance systems, simply parse and index the
surveillance videos. In addition, only one-
dimensional indexing can be performed, separately
on respective pieces of footage captured by a
plurality of cameras, regardless of relationships
between several correlated pieces of footage.
To meet aforementioned problems, the intelligent
surveillance video databases have recently been
developed as a significant component of the
intelligent video surveillance system. Su et al. (2009)
proposed the surveillance video segmentation
method based on moving object detection for
surveillance video indexing and retrieval. Le et al.
(2010) provided an analysis on existing research
results (i.e., object and event detection) for
surveillance video retrieval. Yang et al. (2009)
presented the framework and a data model for
CCTV surveillance videos on RDBMS which
provides the function of a surveillance monitoring
system, with a tagging structure for event detection.
Le et al. (2009) proposed novel data model which
consists of two main abstract concepts (objects and
events). Zhang et al. (2009) proposed a framework
for mining and retrieving events. It is based on video
segmentation and object tracking. Despite of great
achievements in surveillance video databases, there
are few attempts for managing the surveillance
videos in centralized manner.
On the other hand, there are on-going efforts to
apply the data cube model, which is a framework for
supporting the Online Analytical Processing (OLAP)
operations on a huge volume of multi-dimensional
numeric dataset, to multimedia dataset such as text
documents, graphs, and news videos (Lin et al.,
2008; Zhang et al., 2009; Gonzalez et al., 2006; Tian
et al., 2008; Arigon et al., 2007; Lee 2008; Lee et al.,
2009).
The primary objective of this paper is to provide
a multimedia warehousing model for managing the
surveillance videos which are acquired by CCTV
cameras at different locations in centralized manner.
The central control centres of surveillance
systems usually manage and maintain a number of
163
Lee H., Park S. and Yoo J..
A Data Cube Model for Surveillance Video Indexing and Retrieval.
DOI: 10.5220/0004612101630168
In Proceedings of the 10th International Conference on Signal Processing and Multimedia Applications and 10th International Conference on Wireless
Information Networks and Systems (SIGMAP-2013), pages 163-168
ISBN: 978-989-8565-74-7
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)