Tracking and data segmentation using a GGIW filter with mixture clustering
2014 (English)In: FUSION 2014 - 17th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers Inc. , 2014, no 6916137Conference paper (Refereed)
Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended target tracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW) probability hypothesis density (PHD) filter, work directly on raw data. In this paper, the GGIW-PHD filter is applied to real world traffic scenarios. To cope with the large amount of data, a mixture clustering approach which reduces the combinatorial complexity and computation time is proposed. The effective segmentation of raw measurements with respect to spatial distribution and motion is demonstrated and evaluated on two different applications: pedestrian tracking from a vehicle and intersection surveillance.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2014. no 6916137
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-116787ISI: 000363896100168ScopusID: 2-s2.0-84910686579ISBN: 9788490123553OAI: oai:DiVA.org:liu-116787DiVA: diva2:801606
17th International Conference on Information Fusion, FUSION 2014