Competitive Gaussian mixture probability hypothesis density filter for multiple target tracking in the presence of ambiguity and occlusion

Yazdian-Dehkordi, Mahdi; Azimifar, Zohreh; Masnadi-Shirazi, Mohammad Ali

doi:10.1049/iet-rsn.2011.0038

Cited by 27 publications

(27 citation statements)

References 12 publications

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“…The thresholds 0.5    and ε = 3 are selected empirically during the experiments. For each simulation-tracking scenario, 100 Monte Carlo runs are performed, where the mean number of target estimate (NTE) errors [27] and the optimal sub-pattern assignment (OSPA) [28] are used to assess the performance of the proposed algorithm. where the true target set and estimated target set are denoted by X k and ˆk X , respectively.…”

Section: Measurement-driven Gaussian Mixture Phd Schemementioning

confidence: 99%

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

Zhang¹,

Ge²,

Yang³

2016

ETRI J

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The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target‐tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement‐driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre‐filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement‐driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

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Section: Measurement-driven Gaussian Mixture Phd Schemementioning

confidence: 99%

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

Zhang¹,

Ge²,

Yang³

2016

ETRI J

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“…Before penalization, the weight matrix should be analysed first to determine the ambiguous weights. In the CGM-PHD tracker [ 20 ], PGM-PHD tracker [ 21 ] and CPGM-PHD tracker [ 22 ], the weight of target i is determined as an ambiguous weight once the total weight

of the i -th row is greater than one. However, this method is not applicable to Case 2 (as stated in Section 2.3 ) since the total weight

may be less than one when targets approach each other.…”

Section: Improved Gm-phd Tracker With Weight Penalizationmentioning

confidence: 99%

“…In other words, the efficiency of the GM-PHD tracker may degrade when targets come near each other. To remedy this, Yazdian-Dehkordi et al proposed a competitive GM-PHD (CGM-PHD) tracker [ 20 ] and a penalized GM-PHD (PGM-PHD) tracker [ 21 ] to refine the weights of the close moving targets in the update step in the GM-PHD filter. However, they did not provide continuous trajectories for the targets.…”

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Zhou

Liu

et al. 2015

Sensors

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Tracking multiple moving targets from a video plays an important role in many vision-based robotic applications. In this paper, we propose an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker with weight penalization to effectively and accurately track multiple moving targets from a video. First, an entropy-based birth intensity estimation method is incorporated to eliminate the false positives caused by noisy video data. Then, a weight-penalized method with multi-feature fusion is proposed to accurately track the targets in close movement. For targets without occlusion, a weight matrix that contains all updated weights between the predicted target states and the measurements is constructed, and a simple, but effective method based on total weight and predicted target state is proposed to search the ambiguous weights in the weight matrix. The ambiguous weights are then penalized according to the fused target features that include spatial-colour appearance, histogram of oriented gradient and target area and further re-normalized to form a new weight matrix. With this new weight matrix, the tracker can correctly track the targets in close movement without occlusion. For targets with occlusion, a robust game-theoretical method is used. Finally, the experiments conducted on various video scenarios validate the effectiveness of the proposed penalization method and show the superior performance of our tracker over the state of the art.

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“…Tracking of multi-sensor multi-target based on the GM-PHD filter is also studied in [22] and tracking of manoeuvring targets using this filter is considered in [23,24]. Recently, Yazdian-Dehkordi et al [25][26][27] proposed the competitive GM-PHD (CGM-PHD) filter and the penalised GM-PHD (PGM-PHD) filter to improve the performance of the GM-PHD filter for tracking closely spaced and occluded targets.…”

Section: Introductionmentioning

confidence: 99%

Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

Yazdian-Dehkordi

Azimifar

2016

IET signal process.

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The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.

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Competitive Gaussian mixture probability hypothesis density filter for multiple target tracking in the presence of ambiguity and occlusion

Cited by 27 publications

References 12 publications

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Novel N ‐scan GM‐PHD‐based approach for multi‐target tracking

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