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• W2907080467 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. Wahlstrom and E. Ozkan, “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." @default.
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• W2907080467 date "2019-03-01" @default.
• W2907080467 modified "2023-10-11" @default.
• W2907080467 title "Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes" @default.
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• W2907080467 doi "https://doi.org/10.1109/tvt.2019.2891006" @default.
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