Results 211 to 220 of about 40,312 (249)
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Data-Driven Probability Hypothesis Density Filter for Visual Tracking
IEEE Transactions on Circuits and Systems for Video Technology, 2008We apply the probability hypothesis density (PHD) filter to track a random number of pedestrians in image sequences. The PHD filter is implemented using particle filter. How to design importance functions of the particle PHD filter remains a challenge, especially when targets can appear, disappear, merge, or split at any time.
Ashraf Kassim +2 more
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The spline probability hypothesis density filter
SPIE Proceedings, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rajiv Sithiravel +4 more
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Probability hypothesis density filter with uncertainty in the probability of detection
Advances in Space Research, 2021Abstract The space around the earth is becoming increasingly populated. Efficient tracking algorithms are hence integral to protect active space assets from collisions. Ground-based measurements are the primary source of information for any tracking algorithm.
Rohith Reddy Sanaga, Carolin Frueh
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SPIE Proceedings, 2006
The probability hypothesis density (PHD) filter, an automatically track-managed multi-target tracker, is attracting increasing but cautious attention. Its derivation is elegant and mathematical, and thus of course many engineers fear it; perhaps that is currently limiting the number of researchers working on the subject. In this paper, we explore
Ozgur Erdinc +2 more
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The probability hypothesis density (PHD) filter, an automatically track-managed multi-target tracker, is attracting increasing but cautious attention. Its derivation is elegant and mathematical, and thus of course many engineers fear it; perhaps that is currently limiting the number of researchers working on the subject. In this paper, we explore
Ozgur Erdinc +2 more
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Passive infrared localization with a Probability Hypothesis Density filter
2010 7th Workshop on Positioning, Navigation and Communication, 2010In passive infrared localization (PIL) humans are located based on their thermal radiation. Thus, an active tag is not required and privacy is guaranteed due to non-identifying sensors. However, in case of multi-target tracking, the non-identifying sensors result in missing associations between targets and measurements.
Jürgen Kemper, Daniel Hauschildt
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Improved cardinalized probability hypothesis density filtering algorithm
Applied Soft Computing, 2014To overcome computerized intractability and imprecise estimation of the standard cardinalized probability hypothesis density (CPHD) filter for multitarget tracking (MTT), an improved CPHD filtering algorithm is proposed in this paper. We apply Sequential Monte Carlo (SMC) method to achieve the closed-form solution in the filtering process as well as to
Bo Li 0059, Fu-Wen Pang
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The Probability Hypothesis Density filter with evidence fusion
Journal of Electronics (China), 2009The original Probability Hypothesis Density (PHD) filter is a tractable algorithm for Multi-Target Tracking (MTT) in Random Finite Set (RFS) frameworks. In this paper, we introduce a novel Evidence PHD (E-PHD) filter which combines the Dempster-Shafer (DS) evidence theory.
Weifeng Liu, Xiaobin Xu
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Implementation of SLAM by probability hypothesis density filter
Optics and Precision Engineering, 2011Traditional Simultaneous Localization and Mapping(SLAM) algorithm is lack of the ability to describe multiple sensor information accurately in a clutter environment,and it is prone to false data association.Therefore,this paper proposes a SLAM algorithm based on Probability Hypothesis Density(PHD) filter to deal with these problems.By taking the sensor
杜航原 DU Hang-yuan +3 more
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The Recursive Spectral Bisection Probability Hypothesis Density Filter
2019Particle filter (PF) is used for multi-target detection and tracking, especially in the context of variable tracking target numbers, high target mobility, and other complex environments, it is difficult to detect, estimate and track targets in these situations. This paper discusses the probability hypothesis density (PHD) filtering which is widely used
Ding Wang, Xu Tang, Qun Wan
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Stochastic Partitioning for Extended Object Probability Hypothesis Density Filters
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2019This paper presents a new likelihood-based partitioning method of the measurement set for the extended object probability hypothesis density (PHD) filter framework. Recent work has mostly relied on heuristic partitioning methods that cluster the measurement data based on a distance measure between the single measurements.
Julian Böhler +3 more
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