2 research outputs found
Vehicle classification based on convolutional networks applied to FM-CW radar signals
This paper investigates the processing of Frequency Modulated-Continuos Wave
(FM-CW) radar signals for vehicle classification. In the last years deep
learning has gained interest in several scientific fields and signal processing
is not one exception. In this work we address the recognition of the vehicle
category using a Convolutional Neural Network (CNN) applied to range Doppler
signature. The developed system first transforms the 1-dimensional signal into
a 3-dimensional signal that is subsequently used as input to the CNN. When
using the trained model to predict the vehicle category we obtain good
performance.Comment: in Proceedings of 1st European Conference on Traffic Mining Applied
to Police Activities (TRAP 2017
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
High quality perception is essential for autonomous driving (AD) systems. To
reach the accuracy and robustness that are required by such systems, several
types of sensors must be combined. Currently, mostly cameras and laser scanners
(lidar) are deployed to build a representation of the world around the vehicle.
While radar sensors have been used for a long time in the automotive industry,
they are still under-used for AD despite their appealing characteristics
(notably, their ability to measure the relative speed of obstacles and to
operate even in adverse weather conditions). To a large extent, this situation
is due to the relative lack of automotive datasets with real radar signals that
are both raw and annotated. In this work, we introduce CARRADA, a dataset of
synchronized camera and radar recordings with range-angle-Doppler annotations.
We also present a semi-automatic annotation approach, which was used to
annotate the dataset, and a radar semantic segmentation baseline, which we
evaluate on several metrics. Both our code and dataset are available online.Comment: 8 pages, 5 figues. Accepted at ICPR 2020. Erratum: results in Table
III have been updated since the ICPR proceedings, models are selected using
the PP metric instead of the previously used PR metri