Multi-Object Tracking Using Labeled Multi-Bernoulli Random Finite Sets
MetadataShow full item record
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, the labeled multi-Bernoulli filter is not prone to the biased cardinality estimate of the multi-Bernoulli filter. The utilization of the class of labeled random finite sets naturally incorporates the estimation of a targets identity or label. Compared to the d-generalized labeled multi-Bernoulli filter, the labeled multi-Bernoulli filter is anefficient approximation which obtains almost the same accuracy at significantly lower computational cost. The performance of thelabeled multi-Bernoulli filter is compared to the multi-Bernoulli filter using simulated data. Further, the real-time capability of the filter is illustrated using real-world sensor data of our experimental vehicle.
Showing items related by title, author, creator and subject.
Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias ...
Jones, B.; Vo, Ba Tuong; Vo, Ba-Ngu (2016)Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and ...
Beard, M.; Reuter, S.; Granström, K.; Vo, Ba-Ngu; Vo, Ba Tuong; Scheel, A. (2016)Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the ...