Tracking multiple speakers using CPHD filter
Proceedings of the 15th international conference on Multimedia
Gaussian mixture CPHD filter with gating technique
Signal Processing
Localization of multiple emitters based on the sequential PHD filter
Signal Processing
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
The bin-occupancy filter and its connection to the PHD filters
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
IEEE Transactions on Signal Processing
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
FISST-SLAM: Finite Set Statistical Approach to Simultaneous Localization and Mapping
International Journal of Robotics Research
Joint detection and estimation of multiple objects from image observations
IEEE Transactions on Signal Processing
A linear multisensor PHD filter using the measurement dimension extension approach
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Visual tracking of multiple targets by multi-bernoulli filtering of background subtracted image data
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Detection-guided multi-target Bayesian filter
Signal Processing
PHD filter based track-before-detect for MIMO radars
Signal Processing
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
A novel track maintenance algorithm for PHD/CPHD filter
Signal Processing
Visual tracking of numerous targets via multi-Bernoulli filtering of image data
Pattern Recognition
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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The probability hypothesis density (PHD) recursion propagates the posterior intensity of the random finite set (RFS) of targets in time. The cardinalized PHD (CPHD) recursion is a generalization of the PHD recursion, which jointly propagates the posterior intensity and the posterior cardinality distribution. In general, the CPHD recursion is computationally intractable. This paper proposes a closed-form solution to the CPHD recursion under linear Gaussian assumptions on the target dynamics and birth process. Based on this solution, an effective multitarget tracking algorithm is developed. Extensions of the proposed closed-form recursion to accommodate nonlinear models are also given using linearization and unscented transform techniques. The proposed CPHD implementations not only sidestep the need to perform data association found in traditional methods, but also dramatically improve the accuracy of individual state estimates as well as the variance of the estimated number of targets when compared to the standard PHD filter. Our implementations only have a cubic complexity, but simulations suggest favorable performance compared to the standard Joint Probabilistic Data Association (JPDA) filter which has a nonpolynomial complexity.