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Open AccessArticle

GLMB Tracker with Partial Smoothing

School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley 6102, Australia
School of Engineering, RMIT University, Melbourne 3000, Australia
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4419;
Received: 9 August 2019 / Revised: 9 October 2019 / Accepted: 10 October 2019 / Published: 12 October 2019
In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters. View Full-Text
Keywords: labeled RFS; RTS smoother; GLMB filter labeled RFS; RTS smoother; GLMB filter
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Nguyen, T.T.D.; Kim, D.Y. GLMB Tracker with Partial Smoothing. Sensors 2019, 19, 4419.

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