Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Evgeny Burnaev; Ivan Koptelov; German Novikov; Timur Khanipov

doi:10.1117/12.2268706

17 March 2017 Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Evgeny Burnaev, Ivan Koptelov, German Novikov, Timur Khanipov

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034103 (2017) https://doi.org/10.1117/12.2268706
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

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.

Citation Download Citation

Evgeny Burnaev, Ivan Koptelov, German Novikov, and Timur Khanipov "Automatic construction of a recurrent neural network based classifier for vehicle passage detection", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034103 (17 March 2017); https://doi.org/10.1117/12.2268706

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