Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking

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

doi:10.1016/j.sigpro.2011.11.016

Cited by 31 publications

(25 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Zhou

Liu

et al. 2015

Sensors

View full text Add to dashboard Cite

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.

show abstract

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%

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Zhou

Liu

et al. 2015

Sensors

View full text Add to dashboard Cite

show abstract

“…Unfortunately, the labeled RFSs filters required super-exponential growth of the number of components to adequately represent the multi-target states, and more complexity than the PHD filter. In [13,14], a PGM-PHD filter is proposed to solve the problem of tracking closely spaced targets, where a penalized weight competition method is adopted under the framework of the GM-PHD filter. The penalized method is used to refine the weights of closely spaced targets in the update step of the GM-PHD filter, and the PGM-PHD filter gets over the defect of the GM-PHD filter.…”

Section: Introductionmentioning

confidence: 99%

“…Three criteria are adopted, which are the estimated target number, the mean number of targets estimation error (NTE) [14], and the optimal sub-pattern assignment (OSPA) [15]. The OSPA distance and NTE can be described as…”

mentioning

confidence: 99%

Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

Zhang

Yang

2017

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Abstract-Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

show abstract

“…The PHD and CPHD filters were proposed as Poisson RFS density approximations of the multi-target posterior, which can be used to estimate the target states by recursively computing the first-order moment of the multi-target posterior probability distribution. The existing closed-form solutions of PHD mainly include particle filter PHD (PF-PHD) [4,5] and the Gaussian mixture PHD (GM-PHD) filter [6], which have opened the door to numerous novel extensions and applications as shown in [7][8][9][10][11][12][13]. Moreover, being different from the PHD and CPHD filters, the multi-target multi-Bernoulli (MeMBer) [1] recursion was recently proposed by Mahler as a tractable approximation to the Bayesian multi-target recursion under low clutter density scenarios, which can achieve multitarget tracking by directly propagating the approximate posterior density of the targets.…”

Section: Introductionmentioning

confidence: 99%

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

Yang

Xiao

et al. 2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter has been demonstrated as a promising algorithm for multi-target tracking, and the multi-model (MM) method has been incorporated into the CBMeMBer filter to solve the problem of multiple maneuvering target tracking. However, it is difficult to construct a proper set of models due to the unknown maneuvering parameters of the targets. Moreover, the number of models may increase exponentially if more unknown parameters have to be taken into account to match the target motion modes, which may lead to prohibitive computational complexity. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the framework of CBMeMBer filters, so that the model with unknown maneuvering parameter can be modified adaptively by using the selected parameter particles. Moreover, a particle labeling technique is introduced in the proposed algorithm in order to obtain the individual target track, which results in the adaptive parameter particle filter CBMeMBer (APPF-CBMeMBer) tracker. Simulation results show that the proposed algorithm can effectively track multiple maneuvering targets with abruptly changing parameters and exhibit better robustness than those of the well-known MM-based approaches.

show abstract

Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking

Cited by 31 publications

References 17 publications

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Tracking Multiple Video Targets with an Improved GM-PHD Tracker

Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

Contact Info

Product

Resources

About