Title:
A Distributed Framework for Spatio-temporal Analysis on Large-scale Camera Networks
A Distributed Framework for Spatio-temporal Analysis on Large-scale Camera Networks
Author(s)
Hong, Kirak
Voelz, Marco
Govindaraju, Venu
Jayaraman, Bharat
Ramachandran, Umakishore
Voelz, Marco
Govindaraju, Venu
Jayaraman, Bharat
Ramachandran, Umakishore
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
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 including video-based surveillance
and emergency response exploit such camera networks to
detect anomalies in real time and reduce collateral damage. A
well-known technique for detecting such anomalies is spatiotemporal
analysis – an inferencing technique employed by
domain experts (e.g., vision researchers) to answer spatio-temporal
queries. Performing spatio-temporal analysis in real-time for a largescale
camera network is challenging. It involves continuously
analyzing the images from distributed cameras to detect signatures,
generating an event by comparing the detected signature
against a database of known signatures, and maintaining a state
transition table that show the spatio-temporal evolution of people
movement through the distributed spaces. Being inherently
distributed, computationally demanding, and dynamic in terms
of resource requirements, such applications are well-positioned
to exploit smart cameras and cloud computing resources.
However, developing such complex distributed applications is
a daunting task for domain experts. In this paper, we propose a distributed framework to
facilitate the development and deployment of spatio-temporal
analysis applications on large-scale camera networks and
backend computing resources. The framework requires the
domain experts to provide a set of handlers that perform
the domain-specific analyses (e.g., signature detection, event
generation, and state update). The runtime system invokes
these handlers automatically in the distributed environment
consisting of smart camera networks and cloud computing
resources. We make the following contributions: (a) a distributed
programming framework for spatio-temporal analysis,
(b) a careful investigation of the computation/communication
costs associated with the large-scale spatio-temporal analysis
to arrive at the scalable system architecture, (c) automatic
resource configuration to cope with the dynamic workload, (d)
a detailed performance evaluation of our system with a view
to supporting scalability and quality of service.
Sponsor
Date Issued
2012
Extent
Resource Type
Text
Resource Subtype
Technical Report