A distributed framework for situation awareness on camera networks

Hong, Kirak

Title:

A distributed framework for situation awareness on camera networks

dc.contributor.advisor	Ramachandran, Umakishore
dc.contributor.author	Hong, Kirak
dc.contributor.committeeMember	Pande, Santosh
dc.contributor.committeeMember	Ammar, Mostafa
dc.contributor.committeeMember	Iftode, Liviu
dc.contributor.committeeMember	Jayaraman, Bharat
dc.contributor.department	Computer Science
dc.date.accessioned	2014-08-27T13:38:18Z
dc.date.available	2014-08-27T13:38:18Z
dc.date.created	2014-08
dc.date.issued	2014-07-01
dc.date.submitted	August 2014
dc.date.updated	2014-08-27T13:38:18Z
dc.description.abstract	With the proliferation of cameras and advanced video analytics, situation awareness applications that automatically generate actionable knowledge from live camera streams has become an important class of applications in various domains including surveillance, marketing, sports, health care, and traffic monitoring. However, despite the wide range of use cases, developing those applications on large-scale camera networks is extremely challenging because it involves both compute- and data-intensive workloads, has latency-sensitive quality of service requirement, and deals with inherent dynamism (e.g., number of faces detected in a certain area) from the real world. To support developing large-scale situation awareness applications, this dissertation presents a distributed framework that makes two key contributions: 1) it provides a programming model that ensures scalability of applications and 2) it supports low-latency computation and dynamic workload handling through opportunistic event processing and workload distribution over different locations and network hierarchy. To provide a scalable programming model, two programming abstractions for different levels of application logic are proposed: the first abstraction at the level of real-time target detection and tracking, and the second abstraction for answering spatio-temporal queries at a higher level. The first programming abstraction, Target Container (TC), elevates target as a first-class citizen, allowing domain experts to simply provide handlers for detection, tracking, and comparison of targets. With those handlers, TC runtime system performs priority-aware scheduling to ensure real-time tracking of important targets when resources are not enough to track all targets. The second abstraction, Spatio-temporal Analysis (STA) supports applications to answer queries related to space, time, and occupants using a global state transition table and probabilistic events. To ensure scalability, STA supports bounded communication overhead of state update by providing tuning parameters for the information propagation among distributed workers. The second part of this work explores two optimization strategies that reduce latency for stream processing and handle dynamic workload. The first strategy, an opportunistic event processing mechanism, performs event processing on predicted locations to provide just-in-time situational information to mobile users. Since location prediction algorithms are inherently inaccurate, the system selects multiple regions using a greedy algorithm to provide highly meaningful information at the given amount of computing resources. The second strategy is to distribute application workload over computing resources that are placed at different locations and various levels of network hierarchy. To support this strategy, the framework provides hierarchical communication primitives and a decentralized resource discovery protocol that allow scalable and highly adaptive load balancing over space and time.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/52263
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Distributed framework
dc.subject	Situation awareness
dc.subject	Camera networks
dc.subject	Stream processing
dc.title	A distributed framework for situation awareness on camera networks
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Ramachandran, Umakishore
local.contributor.corporatename	College of Computing
relation.isAdvisorOfPublication	ecee44ae-00f0-4d06-b7f7-0967613ef340
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
thesis.degree.level	Doctoral