Results 131 to 140 of about 480,134 (154)

Tracking multiple speakers using CPHD filter

Proceedings of the 15th ACM international conference on Multimedia, 2007
In this paper, we present an efficient method for tracking multiple speakers in a reverberant environment. The proposed method is based on the cardinalized probability hypothesis density (CPHD) filter. Because the CPHD filter can handle a large amount of clutter measurements, our method has a high reliability when tracking multiple speakers. Simulation
Nam Trung Pham, Weimin Huang 0002, S. H. Ong 0001   +2 more
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Approximate multisensor CPHD and PHD filters

2010 13th International Conference on Information Fusion, 2010
The probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter are principled approximations of the general multitarget Bayes recursive filter. Both filters are single-sensor filters. Since their multisensor generalizations are computationally intractable, a further approximation-iterating their corrector ...
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A fast implementation of distributed fusion with CPHD filter

2017 International Conference on Control, Automation and Information Sciences (ICCAIS), 2017
The paper addresses a fast implementation algorithm about distributed fusion with CPHD filter. An implementation method of distributed fusion based on maximum probability association (MPA), called MPA-DF, is presented. Though the performance of MPA-DF is a little worse than traditional Generalization Covariance Intersection (GCI) distributed fusion ...
Guchong Li, Wei Yi 0002, Lingjiang Kong
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CPHD filter addressing occlusions with pedestrians and vehicles tracking

2013 IEEE Intelligent Vehicles Symposium (IV), 2013
In this paper, the problem of targets road tracking, like pedestrians and vehicles tracking is addressed. This paper proposes to improve a Cardinalized Probability Hypothesis Density (CPHD) filter in presence of occlusion using the sensor classification of each targets detected.
Laetitia Lamard, Roland Chapuis, Jean-Philippe Boyer   +2 more
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CPHD filters for superpositional sensors

SPIE Proceedings, 2009
The probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters were introduced as approximations of the full multitarget Bayes detection and tracking filter. Both filters are based on the "standard" multitarget measurement model that underlies most multitarget tracking theory. That is, sensor measurements are presumed to be detections.
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CPHD filtering with unknown probability of detection

SPIE Proceedings, 2010
The conventional PHD and CPHD filters presume that the probability pD(x) that a measurement will be collected from a target with state-vector x (the state-dependent probability of detection) is known a priori. However, in many applications this presumption is false.
Ronald Mahler, Adel El-Fallah
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A survey of PHD filter and CPHD filter implementations

SPIE Proceedings, 2007
The probability hypothesis density (PHD) filter has attracted increasing interest since the author first introduced it in 2000. Potentially practical computational implementations of this filter have been devised, based on sequential Monte Carlo or on Gaussian mixture techniques.
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Adaptive Target Birth Intensity for PHD and CPHD Filters

IEEE Transactions on Aerospace and Electronic Systems, 2012
The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori. In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space. This paper
Branko Ristic 0001, Daniel E. Clark, Ba-Ngu Vo, Ba-Tuong Vo   +3 more
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A novel track maintenance algorithm for PHD/CPHD filter

Signal Processing, 2012
Probability hypothesis density (PHD) filter and cardinalized PHD (CPHD) filter have proved to be promising algorithms for tracking an unknown number of targets in real time. However, they do not provide the identities of the individual estimated targets, so the target tracks cannot be obtained.
Jinlong Yang, Hongbing Ji
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Hybrid multi-Bernoulli and CPHD filters for superpositional sensors

IEEE Transactions on Aerospace and Electronic Systems, 2015
In this paper we present an approximate multi-Bernoulli filter and an approximate hybrid multi-Bernoulli cardinalized probability hypothesis density filter for superpositional sensors. The approximate-filter equations are derived by assuming that the predicted and posterior multitarget states have the same form and propagating the probability ...
Santosh Nannuru, Mark Coates
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