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Proceedings Paper

Automatic construction of a recurrent neural network based classifier for vehicle passage detection
Author(s): Evgeny Burnaev; Ivan Koptelov; German Novikov; Timur Khanipov
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Paper Abstract

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Paper Details

Date Published: 17 March 2017
PDF: 6 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034103 (17 March 2017); doi: 10.1117/12.2268706
Show Author Affiliations
Evgeny Burnaev, Skoltech (Russian Federation)
Institute for Information Transmission Problems (Russian Federation)
Ivan Koptelov, Institute for Information Transmission Problems (Russian Federation)
German Novikov, Institute for Information Transmission Problems (Russian Federation)
Timur Khanipov, Institute for Information Transmission Problems (Russian Federation)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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