A Distributed System for Supporting Spatio-temporal Analysis on Large-scale Camera Networks
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Abstract
Cameras are becoming ubiquitous. Technological
advances and the low cost of such sensors enable deployment
of large-scale camera networks in metropolises such as London
and New York. Applications such as video-base surveillance
and emergency response that exploit such camera networks are
continuous, data intensive, and dynamic in terms of resource
requirements. Common anomalies in such application spaces include
authorized personnel moving into unauthorized spaces and
checking the movement of suspicious individuals as they move
through the spaces. High level goal in such applications include
catching such anomalies in real time and reducing collateral
damage. A well-known technique for meeting this high level
goal is spatio-temporal analysis. This is an inferencing technique
employed by domain experts (e.g., vision researchers) to answer
queries such as show the track of person A in the last 30 minutes.
Performing spatio-temporal analysis in real-time for a large-scale
camera network is challenging. It involves continuously
capturing images from distributed cameras, analyzing the images
to detect and track objects of interest in the field of view of
the cameras, generating an event by comparing the signature of
a detected object against a database of known signatures, and
maintaining a state transition table indexed by time that shows
the spatio-temporal evolution of people movement through the
distributed spaces. In this paper, we propose a distributed system
architecture to address these challenges. We make the following
contributions: (a) present the design choices for real-time spatio-temporal
analysis with a view to supporting scalability (in terms
of number of cameras, event rate, and known targets), (b) develop
heuristics for pruning the event generation phase of spatio-temporal
analysis, and (c) implement and evaluate the different
design choices in a distributed system to show the scalability of
our distributed system architecture.
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Date
2012
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Technical Report