17 March 2017 Automatic construction of a recurrent neural network based classifier for vehicle passage detection
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Evgeny Burnaev, Evgeny Burnaev, Ivan Koptelov, Ivan Koptelov, German Novikov, German Novikov, Timur Khanipov, 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); doi: 10.1117/12.2268706; https://doi.org/10.1117/12.2268706
PROCEEDINGS
6 PAGES


SHARE
Back to Top