Automatic Construction of a Recurrent Neural Network based Classifier   for Vehicle Passage Detection
  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. 
Evgeny Burnaev and Ivan Koptelov and German Novikov and Timur Khanipov
cs.CV cs.LG stat.ML