Multi-object tracking using labeled multi-Bernoulli random finite sets
Stephan Reuter, Ba-Tuong Vo, Ba‐Ngu Vo, Klaus Dietmayer · eSpace (Curtin University) · 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, 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.