Multi-Object Tracking Using Labeled Multi-Bernoulli Random Finite Sets
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Online Access: | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6916141 http://hdl.handle.net/20.500.11937/42972 |
| Summary: | 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. |
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