Radar Multi Object Tracking using DNN Features

Hassan, S.M.; Fioranelli, F.; Yarovoy, Marco Spirito Alexander; Ravindran, Satish

Radar Multi Object Tracking using DNN Features

Conference paper (2023)

Authors

S.M. Hassan

F. Fioranelli

Marco Spirito Alexander Yarovoy

Satish Ravindran NXP Semiconductors

Data association Detector Track-by-detection

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Published Date

2023

Language

English

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Abstract

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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