The adaptable buffer algorithm for high quantile estimation in non-stationary data streams Arandjelović, Ognjen, Pham, Duc-Son and Venkatesh, Svetha 2015, The adaptable buffer algorithm for high quantile estimation in non-stationary data streams, in IJCNN 2015: Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1-7, doi: 10.1109/IJCNN.2015.7280314. Attached Files Name Description MIMEType Size Downloads Title The adaptable buffer algorithm for high quantile estimation in non-stationary data streams Author(s) Arandjelović, Ognjen Pham, Duc-Son Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name International Joint Conference on Neural Networks (2015 : Killarney, Ireland) Conference location Killarney, Ireland Conference dates 12-17 Jul. 2015 Title of proceedings IJCNN 2015: Proceedings of the International Joint Conference on Neural Networks Publication date 2015 Start page 1 End page 7 Total pages 7 Publisher IEEE Place of publication Piscataway, N.J. Summary The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the memory for storing observations is limited. We make several major contributions: (i) we derive an important theoretical result which shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data, (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings, (iii) we introduce a novel algorithm which implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity, and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic data sets and three large 'real-world' streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high valued and the available buffer capacity severely limited. ISBN 9781479919604 Language eng DOI 10.1109/IJCNN.2015.7280314 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30081885 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 04 Mar 2016, 14:09:58 EST Fri, 04 Mar 2016, 14:12:49 EST Fri, 04 Mar 2016, 14:13:40 EST Sat, 05 Mar 2016, 04:00:35 EST Sat, 05 Mar 2016, 05:16:34 EST Tue, 12 Apr 2016, 10:01:59 EST Tue, 12 Apr 2016, 10:04:00 EST Mon, 18 Jul 2016, 16:01:28 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 457 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Fri, 04 Mar 2016, 14:13:40 EST

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