Detection and Tracking of Multiple Metallic Objects in Millimetre-Wave Images
International Journal of Computer Vision
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
Tracking random finite objects using 3D-LIDAR in marine environments
Proceedings of the 2010 ACM Symposium on Applied Computing
Multi-target tracking with poisson processes observations
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
An ant stochastic decision based particle filter and its convergence
Signal Processing
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Bayesian single-target tracking techniques can be extended to a multiple-target environment by viewing the multiple-target state as a random finite set, but evaluating the multiple-target posterior distribution is currently computationally intractable for real-time applications. A practical alternative to the optimal Bayes multitarget filter is the probability hypothesis density (PHD) filter, which propagates the first-order moment of the multitarget posterior instead of the posterior distribution itself. It has been shown that the PHD is the best-fit approximation of the multitarget posterior in an information-theoretic sense. The method avoids the need for explicit data association, as the target states are viewed as a single global target state, and the identities of the targets are not part of the tracking framework. Sequential Monte Carlo approximations of the PHD using particle filter techniques have been implemented, showing the potential of this technique for real-time tracking applications. This paper presents mathematical proofs of convergence for the particle filtering algorithm and gives bounds for the mean-square error