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

A recurrent CNN for online object detection on raw radar frames