A generalized labeled multi-bernoulli filter with object spawning
MetadataShow full item record
Previous labeled random finite set filter developments use a motion model that only accounts for survival and birth. While such a model provides the means for a multi-object tracking filter, such as the generalized labeled multi-Bernoulli (GLMB) filter to capture object births and deaths in a wide variety of applications, it lacks the capability to capture spawned tracks and their lineages. In this paper, we propose a new Generalized Labeled Multi-Bernoulli (GLMB)-based filter that formally incorporates spawning, in addition to birth. This formulation enables the joint estimation of a spawned object's state and information regarding its lineage. Simulations results demonstrate the efficacy of the proposed formulation.
Showing items related by title, author, creator and subject.
Bryant, D.; Vo, Ba Tuong; Vo, Ba-Ngu; Jones, B. (2017)© 2017 IEEE. Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the ...
Dat Nguyen, T.; Kim, Du Yong (2018)© 2018 IEEE. In this paper, we propose an algorithm for tracking cells that also provides lineage information. Our approach incorporates cell spawning into the random finite set dynamic model of the cell population, which ...
Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)In this paper, we propose the labeled multi-Bernoulli filter which explicitly estimates target tracks and provides a more accurate approximation of the multi-object Bayes update than the multi-Bernoulli filter. In particular, ...