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The GM-PHD Filter Multiple Target Tracker
2006 9th International Conference on Information Fusion, 2006 The Gaussian mixture probability hypothesis density filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states from a noisy sequence of sets of measurements which may have missed detections and false alarms.
Daniel E. Clark, Kusha Panta, Ba-Ngu Vo
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GM-PHD filter multitarget tracking in sonar images
SPIE Proceedings, 2006 The Gaussian Mixure Probability Hypothesis Density (GM-PHD) Multi-target Tracker was developed as an extension to the GM-PHD filter to provide track continuity. The algorithm is demonstrated on forward-looking sonar data with clutter and is compared with the results from the Particle PHD filter.
Daniel Clark, Ba-Ngu Vo, Judith Bell
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Tracking Rectangular Targets in Surveillance Videos with the GM-PHD Filter
, 2009 This paper describes the application of a Gaussian Mixture Probability Hypoth- esis Density (GM-PHD) ??lter for tracking objects in surveillance video. Clark et al. have proposed a point-based GM-PHD ??lter designed for track label consis- tency. However, this cannot be used for track consistency when using rectangles covering an object.
Vijverberg, J.A. +2 more
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GM‐PHD Filter With Signal Features Of Emitter
Asian Journal of Control, 2014AbstractA new GM‐PHD filter for multiple emitter targets tracking is proposed in this paper. It integrates the signal features of emitter into the process of weights update. In the case of unknowing the distribution of signal features, the FCM algorithm is used for reference to calculate the correlation coefficients between the measurements and ...
Zhu, Youqing, Zhou, Shilin
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GM‐PHD Filter with State‐Dependent Clutter
Asian Journal of Control, 2016AbstractIn traditional filtering methods, clutter is often assumed to obey a uniform distribution over the entire monitoring area. For many sensors, however, clutter may concentrate in target‐containing regions. Under this condition, the performance of the traditional multi‐target tracking filter can be degraded.
Chen, Jinguang +4 more
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Tracking cell motion using GM-PHD
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009We present a method for tracking the movement of multiple cells and their lineage. We use the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn.
Radford Juang +2 more
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Passive multi target tracking with GM-PHD filter
2010 13th International Conference on Information Fusion, 2010This paper considers the challenging problem of multitarget tracking with passive data, obtained here by geographically distributed cameras. We use a Gaussian Mixture Probability Hypothesis Density filter approach to solve this difficult problem. As we make no spatial assumptions for the birth process, we use a slightly modified filter to obtain our ...
Dann Laneuville, Jeremie Houssineau
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An improvement on GM-PHD filter for occluded target tracking
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011The Probability Hypothesis Density (PHD) filter is the first-order momentum of Bayesian multi-target filter. The Gaussian Mixture PHD (GM-PHD) implementation is a closed form solution for the PHD filter. When targets are too close to each other, such as occlusion condition, the performance of the GM-PHD filter degrades significantly.
Mahdi Yazdian Dehkordi +2 more
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