Generalised labelled multi-Bernoulli forward-backward smoothing
MetadataShow full item record
This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB filter, followed by backward propagation of a corrector that is used to obtain the smoothed GLMB density. The smoother is derived under the assumptions of the standard multi-object dynamic model, and the standard multi-object measurement likelihood model, i.e. The proposed smoother is capable of handling an unknown and time-varying number of objects, in the presence of measurement origin uncertainty, clutter, and missed detections.
Showing items related by title, author, creator and subject.
Papi, Francesco; Ba-Ngu, V.; Ba-Tuong, V.; Fantacci, C.; Beard, M. (2015)In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the ...
Long, Q.; Wu, Changzhi; Huang, T.; Wang, Xiangyu (2015)In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional ...
Kim, Du Yong; Vo, Ba-Ngu; Vo, Ba Tuong; Jeon, M. (2019)© 2019 This paper proposes an online multi-object tracking algorithm for image observations using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, handling of false positives, ...