Spatio-temporal Gaussian process models for extended and group object tracking with irregular shapes

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number8601344
Pages (from-to)2137-2151
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number3
Early online date2019
Publication statusPublished - 1 Mar 2019
MoE publication typeA1 Journal article-refereed

Researchers

Research units

  • University of Sheffield

Abstract

Extended object tracking has become an integral part of many autonomous systems during the last two decades. For the first time, this paper presents a generic spatio-temporal Gaussian process (STGP) for tracking an irregular and non-rigid extended object. The complex shape is represented by key points and their parameters are estimated both in space and time. This is achieved by a factorization of the power spectral density function of the STGP covariance function. A new form of the temporal covariance kernel is derived with the theoretical expression of the filter likelihood function. Solutions to both the filtering and the smoothing problems are presented. A thorough evaluation of the performance in a simulated environment shows that the proposed STGP approach outperforms the state-of-the-art GP extended Kalman filter approach [N. Wahlström and E. Özkan, 'Extended target tracking using Gaussian processes, IEEE Transactions on Signal Processing,' vol. 63, no. 16, pp. 4165-4178, Aug. 2015] with up to 90% improvement in the accuracy in position, 95% in velocity and 7% in the shape, while tracking a simulated asymmetric non-rigid object. The tracking performance improvement for a non-rigid irregular real object is up to 43% in position, 68% in velocity, 10% in the recall, and 115% in the precision measures.

    Research areas

  • Extended Object Tracking, Spatio-temporal Gaussian Process, Rauch-Tung-Striebel smoothing

ID: 31067102