Interacting Methods for Manoeuvre Handling in the GM-PHD Filter

Wood, Trevor

doi:10.1109/taes.2011.6034685

Cited by 17 publications

(10 citation statements)

References 9 publications

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“…Such as linear jump Markov system (JMS) for GM-PHD filter [125,126] and SMC-PHD [127,128], nonlinear JMS models for PHD filter [129]. Also, the IMM method for GM-PHD filter [130], and the MM method for GM-CPHD filter [131], are also presented in the literature.…”

Section: ) Maneuvering Target Motion Modelingmentioning

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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Section: ) Maneuvering Target Motion Modelingmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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“…This might be modeled as a jump Markov linear system, meaning that there are two different motion modes and switching between these two is a Markov process. Handling jump Markov linear systems within the PHD filter has been presented in [21], [23], and [37] for GM-PHD, SMC-PHD, and the general case, respectively. This method may be used to improve handling of rapid changes in behavior while tracking but is omitted here for simplicity as the bacterial strain used did not display stopping behavior.…”

Section: B Tracking Scenariomentioning

confidence: 99%

Simplified Multitarget Tracking Using the PHD Filter for Microscopic Video Data

Wood

Yates

Wilkinson

et al. 2012

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-The probability hypothesis density (PHD) filter from the theory of random finite sets is a well-known method for multitarget tracking. We present the Gaussian mixture (GM) and improved sequential Monte Carlo implementations of the PHD filter for visual tracking. These implementations are shown to provide advantages over previous PHD filter implementations on visual data by removing complications such as clustering and data association and also having beneficial computational characteristics. The GM-PHD filter is deployed on microscopic visual data to extract trajectories of free-swimming bacteria in order to analyze their motion. Using this method, a significantly larger number of tracks are obtained than was previously possible. This permits calculation of reliable distributions for parameters of bacterial motion. The PHD filter output was tested by checking agreement with a careful manual analysis. A comparison between the PHD filter and alternative tracking methods was carried out using simulated data, demonstrating superior performance by the PHD filter in a range of realistic scenarios.Index Terms-Bacterial motion, multitarget tracking, probability hypothesis density (PHD) filter, random finite sets, sequential Monte Carlo.

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“…Specifically, the main idea is to assign a class-matched PHD-like filter for each possible class to estimate the class-conditioned PHD, which is also inspired by the previous work in [12], [20] that is devoted to the single-target JTC problem based on a classical Bayesian approach. So far, there are several PHD-like filters, such as a classical PHD filter for steady multitarget tracking, its multiple-model version for linear Gaussian jump Markov system multitarget models [28,29], and the MMPHD filter [11]. Considering that the particle implementation accommodates more general dynamic and measurement models, the following paper will adopt the particle versions of the classical PHD and the MMPHD filters for steady target classes and maneuvering target classes, respectively.…”

Section: B the Recursive Multitarget Jdtc Algorithmmentioning

confidence: 99%

Joint Detection, Tracking, and Classification of Multiple Targets in Clutter using the PHD Filter

Yang

Jianqian

et al. 2012

IEEE Trans. Aerosp. Electron. Syst.

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To account for joint detection, tracking, and classification (JDTC) of multiple targets from a sequence of noisy and cluttered observation sets, this paper introduces a recursive algorithm based on the probability hypothesis density (PHD) filter with the particle implementation. Assuming that each target class has a class-dependent kinematic model set, a class-matched PHD-like filter (i.e., PHD filter or its multiple-model implementation (MMPHD)) is assigned to it. In the prediction stage, the particles are propagated according to their class-dependent kinematic model set in the matched PHD-like filter. Then, the mutual information exchange between these PHD-like filters is completed by updating the particle weights in the update stage. The particles with the same class label and their corresponding weights represent the estimated class-conditioned PHD distribution. These class-conditioned PHD distributions are used to jointly estimate the number of the corresponding class targets and their states. Moreover, the algorithm incorporates the feature measurements into these PHD-like filters. The proposed multitarget JDTC algorithm has four distinctive features. First, it has a flexible modularized structure, i.e., it assigns a class-matched PHD-like filter for each target class, and facilitates the incorporation of the extra PHD-like filter for a new target class. Second, the particles can be propagated according to their exact class-dependent kinematic model set thanks to the modularized structure. Third, because of the feature measurements added and no explicit associations, it can track multiple closely spaced targets from different classes. Fourth, it avoids the possibility that the target classes with temporarily low likelihoods can end up being permanently lost. The computational burden of the proposed algorithm is linearly increased with the class number of targets. The algorithm is illustrated via a simulation example involving the tracking of two closely spaced parallel moving targets and two crossing moving targets from different classes, where targets can appear and disappear.

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Interacting Methods for Manoeuvre Handling in the GM-PHD Filter

Cited by 17 publications

References 9 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Simplified Multitarget Tracking Using the PHD Filter for Microscopic Video Data

Joint Detection, Tracking, and Classification of Multiple Targets in Clutter using the PHD Filter

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