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arXiv · 1609.08209

Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection

Evgeny Burnaev·Ivan Koptelov·German Novikov·Timur Khanipov
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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.

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Evgeny Burnaev, Ivan Koptelov, German Novikov, Timur Khanipov. 2016-09-26. Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection. https://arxiv.org/abs/1609.08209

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