Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters

Ana Stroescu; Liam Daniel; Marina Gashinova

doi:10.1109/RadarConf2043947.2020.9266686

Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters

Ana Stroescu, Liam Daniel, Marina Gashinova

Electronic, Electrical and Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a novel approach for target detection in radar imagery, which combines an object detector and a multi target particle filter tracker. Object detection is implemented using deep neural networks, as opposed to the traditional radar object detection methods. This technique is applied to a dataset collected with a 79 GHz FMCW radar mounted on a vehicle. In this approach, object detection and tracking of roadside objects are performed in an alternating fashion to reduce the computational load required by the real time processing. The results and the thorough analysis of the parameters showed that this approach is feasible and can be successfully utilised in radar imagery for autonomous driving.

Original language	English
Title of host publication	2020 IEEE Radar Conference, RadarConf 2020
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781728189420
DOIs	https://doi.org/10.1109/RadarConf2043947.2020.9266686
Publication status	Published - 21 Sept 2020
Event	2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy Duration: 21 Sept 2020 → 25 Sept 2020

Publication series

Name	IEEE National Radar Conference - Proceedings
Volume	2020-September
ISSN (Print)	1097-5659

Conference

Conference	2020 IEEE Radar Conference, RadarConf 2020
Country/Territory	Italy
City	Florence
Period	21/09/20 → 25/09/20

Bibliographical note

Funding Information:
ACKNOWLEDGMENT The radar data acquisition was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012372/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme.

Publisher Copyright:
© 2020 IEEE.

Keywords

Autonomous Driving
Deep Neural Networks
Multi Target Tracking
Object Detection
Particle Filter
Radar
Track Before Detect

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/RadarConf2043947.2020.9266686

Cite this

Stroescu, A., Daniel, L., & Gashinova, M. (2020). Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters. In 2020 IEEE Radar Conference, RadarConf 2020 Article 9266686 (IEEE National Radar Conference - Proceedings; Vol. 2020-September). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/RadarConf2043947.2020.9266686

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abstract = "This paper presents a novel approach for target detection in radar imagery, which combines an object detector and a multi target particle filter tracker. Object detection is implemented using deep neural networks, as opposed to the traditional radar object detection methods. This technique is applied to a dataset collected with a 79 GHz FMCW radar mounted on a vehicle. In this approach, object detection and tracking of roadside objects are performed in an alternating fashion to reduce the computational load required by the real time processing. The results and the thorough analysis of the parameters showed that this approach is feasible and can be successfully utilised in radar imagery for autonomous driving.",

keywords = "Autonomous Driving, Deep Neural Networks, Multi Target Tracking, Object Detection, Particle Filter, Radar, Track Before Detect",

author = "Ana Stroescu and Liam Daniel and Marina Gashinova",

note = "Funding Information: ACKNOWLEDGMENT The radar data acquisition was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012372/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Radar Conference, RadarConf 2020 ; Conference date: 21-09-2020 Through 25-09-2020",

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Stroescu, A, Daniel, L & Gashinova, M 2020, Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters. in 2020 IEEE Radar Conference, RadarConf 2020., 9266686, IEEE National Radar Conference - Proceedings, vol. 2020-September, Institute of Electrical and Electronics Engineers (IEEE), 2020 IEEE Radar Conference, RadarConf 2020, Florence, Italy, 21/09/20. https://doi.org/10.1109/RadarConf2043947.2020.9266686

Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters. / Stroescu, Ana; Daniel, Liam ; Gashinova, Marina.
2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. 9266686 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: ACKNOWLEDGMENT The radar data acquisition was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012372/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme. Publisher Copyright: © 2020 IEEE.

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AB - This paper presents a novel approach for target detection in radar imagery, which combines an object detector and a multi target particle filter tracker. Object detection is implemented using deep neural networks, as opposed to the traditional radar object detection methods. This technique is applied to a dataset collected with a 79 GHz FMCW radar mounted on a vehicle. In this approach, object detection and tracking of roadside objects are performed in an alternating fashion to reduce the computational load required by the real time processing. The results and the thorough analysis of the parameters showed that this approach is feasible and can be successfully utilised in radar imagery for autonomous driving.

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Stroescu A, Daniel L , Gashinova M. Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters. In 2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers (IEEE). 2020. 9266686. (IEEE National Radar Conference - Proceedings). doi: 10.1109/RadarConf2043947.2020.9266686

Combined Object Detection and Tracking on High Resolution Radar Imagery for Autonomous Driving Using Deep Neural Networks and Particle Filters

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this