Novel data association schemes for the probability hypothesis density filter

Panta, K.; Vo, Ba-Ngu; Singh, Sumeetpal S.

doi:10.1109/taes.2007.4285353

Cited by 137 publications

(74 citation statements)

References 14 publications

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“…In this application which the clutter rate and detection probability are time-varying, we demonstrated that our bootstrap filter is able to better track the real targets and more reliably avoid false tracks compared to the other RFS filters such as the MM-PHD, MM-CPHD and MM-λ-p D -CPHD as well as other stateof-the-art particle tracking methods such as the IMM-JPDA, MHT, P-Tracker and U-Tracker in both synthetic and real sequences. However, in the applications where the motion of each individual targets is required, the tag propagation scheme may need to be changed to a track management algorithm, e.g., that proposed in [46,47] or more generally, the tracker in the bootstrap filter can be replaced by any multi-target tracker that requires knowledge of false alarm and detection rate and performs reliably in the label assignments. In the RFS concept, a principled solution to the track labeling problem using labeled RFS [39] has been recently proposed which can be also used for this purpose.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the dynamics of an individual target cannot be evaluated. To deal with this, some authors combine the PHD filter with a track management technique to maintain the identity of tracks [46,47]. To avoid any computational burden due to a track management step, we simply use our tag propagation scheme [16], which only propagates the identity of the intensity distributions for all the PHD and CPHD filters.…”

Section: A Setup and Implementation Detailsmentioning

confidence: 99%

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Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

Rezatofighi

Gould

et al. 2015

IEEE Trans. Med. Imaging

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Abstract-Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.

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Section: Discussionmentioning

confidence: 99%

Section: A Setup and Implementation Detailsmentioning

confidence: 99%

Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

Rezatofighi

Gould

et al. 2015

IEEE Trans. Med. Imaging

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“…Then the peaks are association to target tracks by traditional data association methods, such as PHD/CPHD filtering with MHT [146,147], PHD/CPHD filtering with the connectivity graph and cross entropy (CE) technique [148], CPHD filtering with fuzzy logy [149], 2-D assignment for PHD filtering [150,151] and CPHD filtering [152,153], and PHD/CPHD filtering with graph matching [118,140].…”

Section: ) Target Birth and Spawning Modelmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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“…For the PHD approach, methods performing these extra steps have been reported using particle PHD filters [26][27][28] or Gaussian mixture (GM)-PHD filters [29]. Target positions are typically identified by peak-picking the target intensity function being tracked, and the estimated target positions are treated as measurements for the ensuing data association and track recovery tasks.…”

Section: Position Estimation and Track Formationmentioning

confidence: 99%

Tracking target signal strengths on a grid using sparsity

Farahmand

Giannakis

Leus

et al. 2014

EURASIP J. Adv. Signal Process.

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Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to linear state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through 1 -norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root-mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts. Comparison with the recently developed additive likelihood moment filter reveals the better performance of the proposed TSSG tracker.

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Novel data association schemes for the probability hypothesis density filter

Cited by 137 publications

References 14 publications

Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

A Survey of Motion-Based Multitarget Tracking Methods

Tracking target signal strengths on a grid using sparsity

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