Occluded targets tracking using improved GM-PHD tracker

Adeli, Ali; Yazdian-Dehkordi, Mahdi; Azimifar, Zohreh; Rojhani, Omid Reza

doi:10.1109/icosp.2012.6491763

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Estimate the trajectory of all targets. At present, the track generation method based on PHD filtering is mainly combined with the traditional data correlation technology, and the multi-target RFS estimated at each discrete time is regarded as an "observation set", and then the traditional data correlation technology is used to form the target track, but this method does not Considering the situation of missed detection and false estimation of the target, it will lead to the formation of a false track and a target will produce multiple tracks, which will be detrimental to the comprehensive situation understanding and analysis of the surveillance scene [8]. In the future, how to realize joint state estimation and track generation and extraction under the framework of PHD filtering is a direction worthy of study.…”

Section: Prospect Of Phd Filter Technologymentioning

confidence: 99%

Research and Application of Multi-Target Tracking Based on GM-PHD Filter

Li¹,

Guo²,

Huang³

2020

OPJ

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In recent years, multi-target tracking technology based on Gaussian Mixture-Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fusion research. This article outlines the generation and development of multi-target tracking methods based on GM-PHD filtering, and the principle and implementation method of GM-PHD filtering are explained, and the application status based on GM-PHD filtering is summarized, and the key issues of the development of GM-PHD filtering technology are analyzed.

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Section: Prospect Of Phd Filter Technologymentioning

confidence: 99%

Research and Application of Multi-Target Tracking Based on GM-PHD Filter

Li¹,

Guo²,

Huang³

2020

OPJ

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“…In 2006, Vo et al [4] proposed GM-PHD filter as a closed-form solution of PHD filter which estimates the PHD density using a mixture of Gaussian densities. In recent years, the GM-PHD filter is widely applied on different applications such as radar targets [5], visual targets [6] and extended targets [7]. Tracking of multi-sensor multi-target based on GM-PHD filter is also studied in [8].…”

Section: Introductionmentioning

confidence: 99%

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Yazdian-Dehkordi

Azimifar

2015

2015 23rd Iranian Conference on Electrical Engineering

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Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed as a closed form solution of PHD filter to estimate the first-order moment of the multi-target posterior density. Recently, different methods such as Competitive GM-PHD (CGM-PHD), Penalized GM-PHD (PGM-PHD) and Collaborative PGM-PHD (CPGM-PHD) are proposed to enhance the performance of GM-PHD filter for tracking closely spaced targets. These methods have no assumption about possible subsequent missed detections which occur in some practical applications. For this reason, the performance of these filters degrades in this condition. In this paper, we propose a novel improvement on GM-PHD filter to track targets in possible subsequent missed detections. In addition to targets weight, we define a probability of confirm (PC) for each target which is adaptively calculated in time. We also propose a new state refinement and state extraction methods based on the defined PC. The experimental results provided for different uncertainties show the effectiveness of the proposed method.

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Refined GM-PHD tracker for tracking targets in possible subsequent missed detections

Yazdian-Dehkordi

Azimifar

2015

Signal Processing

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Occluded targets tracking using improved GM-PHD tracker

Cited by 4 publications

References 8 publications

Research and Application of Multi-Target Tracking Based on GM-PHD Filter

Research and Application of Multi-Target Tracking Based on GM-PHD Filter

An improvement on GM-PHD filter for target tracking in presence of subsequent miss-detection

Refined GM-PHD tracker for tracking targets in possible subsequent missed detections

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