  Automotive radar sensors provide valuable information for advanced driving
assistance systems (ADAS). Radars can reliably estimate the distance to an
object and the relative velocity, regardless of weather and light conditions.
However, radar sensors suffer from low resolution and huge intra-class
variations in the shape of objects. Exploiting the time information (e.g.,
multiple frames) has been shown to help to capture better the dynamics of
objects and, therefore, the variation in the shape of objects. Most temporal
radar object detectors use 3D convolutions to learn spatial and temporal
information. However, these methods are often non-causal and unsuitable for
real-time applications. This work presents RECORD, a new recurrent CNN
architecture for online radar object detection. We propose an end-to-end
trainable architecture mixing convolutions and ConvLSTMs to learn
spatio-temporal dependencies between successive frames. Our model is causal and
requires only the past information encoded in the memory of the ConvLSTMs to
detect objects. Our experiments show such a method's relevance for detecting
objects in different radar representations (range-Doppler, range-angle) and
outperform state-of-the-art models on the ROD2021 and CARRADA datasets while
being less computationally expensive.
