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Proceedings Paper

Characterizing the uncertainty of classification methods and its impact on the performance of crowdsourcing
Author(s): Javier Ribera; Khalid Tahboub; Edward J. Delp
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Paper Abstract

Video surveillance systems are widely deployed for public safety. Real-time monitoring and alerting are some of the key requirements for building an intelligent video surveillance system. Real-life settings introduce many challenges that can impact the performance of real-time video analytics. Video analytics are desired to be resilient to adverse and changing scenarios. In this paper we present various approaches to characterize the uncertainty of a classifier and incorporate crowdsourcing at the times when the method is uncertain about making a particular decision. Incorporating crowdsourcing when a real-time video analytic method is uncertain about making a particular decision is known as online active learning from crowds. We evaluate our proposed approach by testing a method we developed previously for crowd flow estimation. We present three different approaches to characterize the uncertainty of the classifier in the automatic crowd flow estimation method and test them by introducing video quality degradations. Criteria to aggregate crowdsourcing results are also proposed and evaluated. An experimental evaluation is conducted using a publicly available dataset.

Paper Details

Date Published: 6 March 2015
PDF: 10 pages
Proc. SPIE 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015, 94080A (6 March 2015); doi: 10.1117/12.2085415
Show Author Affiliations
Javier Ribera, Purdue Univ. (United States)
Khalid Tahboub, Purdue Univ. (United States)
Edward J. Delp, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 9408:
Imaging and Multimedia Analytics in a Web and Mobile World 2015
Qian Lin; Jan P. Allebach; Zhigang Fan, Editor(s)

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