Selecting and evaluating data for training a pedestrian detector for crowded conditions

Simonnet, D, Velastin, S.A., Orwell, J. and Turkbeyler, E (2011) Selecting and evaluating data for training a pedestrian detector for crowded conditions. In: UNSPECIFIED, (ed.) 2011 IEEE International Conference on Signal and Image Processing Applications. IEEE. pp. 174-179. (IEEE International Conference on Signal and Image Processing Applications (ICSIPA)) ISBN 9781457702433

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Abstract

Computer vision algorithms for pedestrian detection are often based on classification derived from supervised learning and therefore require training data, which can be built by using generic or specific images. In this field, INRIA datasets are a standard reference but include only few CCTV camera samples. Therefore, for a CCTV camera system it might be interesting to have specific training data. However, in practice it is impossible to create a training data for each camera view. Thus, this paper presents an evaluation of a pedestrian detection algorithm in crowded conditions in relation to the training data, and shows that a CCTV camera training data provides better results and can be reused for similar CCTV camera views

Item Type: Book Section
Event Title: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Computing, Information Systems and Mathematics (until 2011) > Digital Imaging Research Centre (DIRC)
Faculty of Science, Engineering and Computing (until 2017)
Faculty of Science, Engineering and Computing (until 2017) > School of Computing and Information Systems
Depositing User: Automatic Import Agent
Date Deposited: 11 Feb 2014 13:53
Last Modified: 02 May 2017 10:45
URI: http://eprints.kingston.ac.uk/id/eprint/23509

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