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

Waqas Aftab, Roland Hostettler, Allan De Freitas, Mahnaz Arvaneh, Lyudmila Mihaylova

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


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.

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


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

Fingerprint Dive into the research topics of 'Spatio-temporal Gaussian process models for extended and group object tracking with irregular shapes'. Together they form a unique fingerprint.

  • Cite this