σ-threshold Bayes Filter in Unknown Birth Background with Multi-Bernoulli Finite Sets

Abstract

Multiple object tracking faces a challenge of realistically modelling birth background in the premise of keeping the efficiency of filtering. Existing adaptive birth models only pay attention to modeling the birth density, simply assuming the birth probability (BP) constant, resulting inaccurate birth description and deteriorated tracking performance. Moreover, the adaptive birth models incur much heavier computational burden, which greatly limits the real-time capability. The paper gives an efficient adaptive birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter, capable of truly adapting birth as well as effectively achieving good tracking performance via the adaptive calculation of the BP by pre-processing, and reducing the unnecessary likelihood calculations by a measurement noise (MN)-based threshold.