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A learning approach to interactive browsing of surveillance content

Published: 31 August 2010 Publication History

Abstract

In this paper, we present a novel application for interactive browsing of (recorded) surveillance content. The application is based on user feedback and enables an operator to switch between camera views that are likely to contain the same activity. Our system relies on off-the-shelf background-subtraction activity detection mechanisms. We use two techniques from machine learning to automatically learn the topology of surveillance camera networks. The first technique identifies connections between camera views for which objects are temporarily out of view, while the second technique identifies overlap between views. Testing on an actual surveillance camera network suggests that the approach is both accurate and robust, despite the simplicity of the involved computer vision methods.

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cover image ACM Conferences
ICDSC '10: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
August 2010
252 pages
ISBN:9781450303170
DOI:10.1145/1865987
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 31 August 2010

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ICDSC '10
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ICDSC '10: International Conference on Distributed Smart Cameras
August 31 - September 4, 2010
Georgia, Atlanta

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Overall Acceptance Rate 92 of 117 submissions, 79%

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