A neural network (NN) based multi-frame classification approach is proposed to solve the problem of classification of tracked objects. Initially, a baseline tracker is implemented that uses the classification output of an object detection network for classification. Afterwards, two approaches for multi-frame classification are applied to perform classification of tracked objects. The first approach aggregates points from multiple frames and applies a single frame NN for classification, whereas the second approach uses bidirectional long short term memory (BiLSTM) layers to process points from multiple frames. Extensive experiments on the opensource 2D RadarScenes dataset showed a consistent increase in track performance when using either of the two techniques for multi-frame classification.@en

A single frame radar-based multi-object tracker that aims to improve data association for better tracking performance is proposed. Firstly, a baseline tracker based on track-by-detection paradigm was implemented for automotive radar. Secondly, investigation on the performance of the tracker when tracking individual classes separately versus all classes together was performed. Thirdly, appearance features were extracted from a neural network and added as an additional metric to the cost matrix for improved data association. Extensive experiments on the 2D RadarScenes dataset and a 3D proprietary Lunewave dataset (in partnership with NXP Semiconductors) showed a consistent improvement in the tracking performance using the approach proposed by adding features extracted from a neural network.

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