Joint Detection and Estimation of Multiple Objects From Image Observations

By B. Vo, Nam Trung Pham, David Suter. IEEE Transactions on Signal Processing, 2010

380 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “The RFS approach has attracted significant attention as a general systematic treatment of multi-object systems and provides the foundation for the development of novel filters such as the Probability Hypothesis Density (PHD) filter [5], Cardinalized PHD (CPHD) filter [6], and multi-Bernoulli filters”
The Labeled Multi-Bernoulli Filter (2014, methodology): “Note that the multi-Bernoulli filter in [10] operates on point measurements while the multi-Bernoulli filter in [11] operates on image measurements.”
A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, background): “This is important in signal networks, radar/sonar or video surveillance.”
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “Due to the n umerical complexity of Bayes multi-target filter, the Probab ility Hypothesis Density (PHD) [5], Cardinalized PHD (CPHD) [26], and multi-Bernoulli filters [27], [6] have been develo ped as approximations.”
Labeled Random Finite Sets and Multi-Object Conjugate Priors (2013, background): “Current numerical integration techniques for approximating the normalizing constant are not suitable for online inference, except for scenarios with a small number of objects.”