Efficient multi-sensor extended target tracking using GM-PHD filter

Alireza Ahrabian, Mehryar Emambakhsh, Marcel Sheeny, Andrew Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This work deals with the efficient fusion of multiple disparate sensors, namely THz radar, stereo camera and lidar for autonomous vehicles. In particular, we develop a target tracking algorithm that is object agnostic i.e. we seek to detect any potential object in the scene and track it while also preserving extended target characteristics such as length and width. To this end, we first use conventional clustering and labelling methods in order to generate consistent features from each sensor independently. The features from each sensor are then transformed into a set of bounding boxes located in both range and cross range. The bounding box parameters are then fed into the proposed efficient multi-sensor target tracking algorithm. This is achieved by modifying the Gaussian mixture-PHD filter (GM-PHD) by incorporating a set of class labels that associate a state to a set of sensors. The performance of the proposed method target tracking method is verified using both synthetic and real world data.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Vehicles Symposium (IV)
PublisherIEEE
Pages1731-1738
Number of pages8
ISBN (Electronic)9781728105604
DOIs
Publication statusPublished - 29 Aug 2019
Event30th IEEE Intelligent Vehicles Symposium 2019 - Paris, France
Duration: 9 Jun 201912 Jun 2019

Publication series

NameIEEE Intelligent Vehicles Symposium (IV)
ISSN (Electronic)2642-7214

Conference

Conference30th IEEE Intelligent Vehicles Symposium 2019
CountryFrance
CityParis
Period9/06/1912/06/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

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