Characterizing the uncertainty of classification methods and its impact on the performance of crowdsourcing

Javier Ribera; Khalid Tahboub; Edward J. Delp III

doi:10.1117/12.2085415

6 March 2015 Characterizing the uncertainty of classification methods and its impact on the performance of crowdsourcing

Javier Ribera, Khalid Tahboub, Edward J. Delp III

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Proceedings Volume 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015; 94080A (2015) https://doi.org/10.1117/12.2085415
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Javier Ribera, Khalid Tahboub, and Edward J. Delp III "Characterizing the uncertainty of classification methods and its impact on the performance of crowdsourcing", Proc. SPIE 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015, 94080A (6 March 2015); https://doi.org/10.1117/12.2085415

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KEYWORDS

Video surveillance

Video

Analytics

Image segmentation

Intelligence systems

Video compression

Surveillance

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