Track-to-track association for intelligent vehicles by preserving local track geometry

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dc.contributor.author Zou, Ke
dc.contributor.author Zhu, Hao
dc.contributor.author De Freitas, Allan
dc.contributor.author Li, Yongfu
dc.contributor.author Najafabadi, Hamid Esmaeili
dc.date.accessioned 2021-04-08T10:55:26Z
dc.date.available 2021-04-08T10:55:26Z
dc.date.issued 2020-03-04
dc.description The authors gratefully acknowledge the Autonomous Vision Group for providing the KITTI dataset. The authors also would like to thank the editors and referees for the valuable comments and suggestions. en_ZA
dc.description.abstract Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation–maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods. en_ZA
dc.description.department Electrical, Electronic and Computer Engineering en_ZA
dc.description.librarian am2021 en_ZA
dc.description.sponsorship The Research Funds of Chongqing Science and Technology Commission, the National Natural Science Foundation of China, the Key Project of Crossing and Emerging Area of CQUPT, the Research Fund of young-backbone university teacher in Chongqing province, Chongqing Overseas Scholars Innovation Program, Wenfeng Talents of Chongqing University of Posts and Telecommunications, Innovation Team Project of Chongqing Education Committee, the National Key Research and Development Program, the Research and Innovation of Chongqing Postgraduate Project, the Lilong Innovation and Entrepreneurship Fund of Chongqing University of Posts and Telecommunications. en_ZA
dc.description.uri http://www.mdpi.com/journal/sensors en_ZA
dc.identifier.citation Zou, K., Zhu, H., De Freitas, A. et al. 2020, 'Track-to-track association for intelligent vehicles by preserving local track geometry', Sensors, vol. 20, art. 1412, pp. 1-17. en_ZA
dc.identifier.issn 1424-8220 (online)
dc.identifier.other 10.3390/s20051412
dc.identifier.uri http://hdl.handle.net/2263/79355
dc.language.iso en en_ZA
dc.publisher MDPI Publishing en_ZA
dc.rights © 2020 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_ZA
dc.subject Gaussian mixture model en_ZA
dc.subject Local track geometry en_ZA
dc.subject Maximum likelihood estimation en_ZA
dc.subject Sensor bias en_ZA
dc.subject Track association en_ZA
dc.subject Track-to-track association (T2TA) en_ZA
dc.title Track-to-track association for intelligent vehicles by preserving local track geometry en_ZA
dc.type Article en_ZA


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