Von Mises Mixture PHD Filter

Marković, Ivan; Ćesić, Josip

doi:10.1109/lsp.2015.2472962

Cited by 18 publications

(15 citation statements)

References 28 publications

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“…1, using a sampling rate of 12 Hz for plotting. Note that the PHD-based filter method [13] has two caveats. First, observation-to-source assignments cannot be estimated (unless a post-processing step is performed), and second, the estimated source trajectories are not smooth.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The von Mises-Fisher distribution was used in [12] to build a factorial filter. A mixture of von Mises distributions was combined with a PHD filter in [13]. The main drawback of PHD filters is that explicit observation-to-source associations are not established.…”

Section: Introductionmentioning

confidence: 99%

“…Results obtained with recordings #1 (left) and #2 (right) from Task 6 of the LOCATA dataset. Top-to-down: vM-PHD[13], GM-FO[16], vM-VEM (proposed) and groundtruth trajectories. Different colors represent different audio sources.…”

mentioning

confidence: 99%

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Tracking Multiple Audio Sources With the von Mises Distribution and Variational EM

Ban

Alameda-Pineda

Evers

et al. 2019

IEEE Signal Process. Lett.

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In this paper we address the problem of simultaneously tracking several moving audio sources, namely the problem of estimating source trajectories from a sequence of observed features. We propose to use the von Mises distribution to model audio-source directions of arrival with circular random variables. This leads to a Bayesian filtering formulation which is intractable because of the combinatorial explosion of associating observed variables with latent variables, over time. We propose a variational approximation of the filtering distribution. We infer a variational expectation-maximization algorithm that is both computationally tractable and time efficient. We propose an audio-source birth method that favors smooth source trajectories and which is used both to initialize the number of active sources and to detect new sources. We perform experiments with the recently released LOCATA dataset comprising two moving sources and a moving microphone array mounted onto a robot.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking Multiple Audio Sources With the von Mises Distribution and Variational EM

Ban

Alameda-Pineda

Evers

et al. 2019

IEEE Signal Process. Lett.

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“…Besides Gaussians, other distributions can be used and in our previous work [30] we proposed a mixture approximation of the PHD filter based on the von Mises distribution on the unit circle. In this letter, as a study example, we implement a PHD filter tailored for Lie groups (LG-PHD), based on the mixture of CGDs and the reduction schemes presented in the previous section.…”

Section: Study Example -Phd Filter On Lie Groupsmentioning

confidence: 99%

Mixture Reduction on Matrix Lie Groups

Ćesić

Marković

2017

IEEE Signal Process. Lett.

Self Cite

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Abstract-Many physical systems evolve on matrix Lie groups and mixture filtering designed for such manifolds represent an inevitable tool for challenging estimation problems. However, mixture filtering faces the issue of a constantly growing number of components, hence require appropriate mixture reduction techniques. In this letter we propose a mixture reduction approach for distributions on matrix Lie groups, called the concentrated Gaussian distributions (CGDs). This entails appropriate reparametrization of CGD parameters to compute the KL divergence, pick and merge the mixture components. Furthermore, we also introduce a multitarget tracking filter on Lie groups as a mixture filtering study example for the proposed reduction method. In particular, we implemented the probability hypothesis density filter on matrix Lie groups. We validate the filter performance using the optimal subpattern assignment metric on a synthetic dataset consisting of 100 randomly generated multitarget scenarios.

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“…Notably, due to its simplicity and effectiveness, the shape of a trajectory is one of the commonly used descriptions for trajectory analysis and anomaly detection [7][8][9], and our paper focuses on shape analysis and anomaly detection. The shape of a motion trajectory is classically characterized by a series of tangential angles at the positions of the corresponding moving object (for the sake of easy description, in this paper, we call these positions as the sample points of the trajectory), and these angles are usually modeled by using the von Mises distribution to represent the trajectory [7,8,10]. However, despite the fact that the angle attribute is largely helpful for a lot of practical applications, the use of only this single attribute cannot discriminate, for instance, whether a person is walking or running along a same path in an automated surveillance system.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools

Guo

Sbert

et al. 2017

Entropy

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In this paper, we propose to improve trajectory shape analysis by explicitly considering the speed attribute of trajectory data, and to successfully achieve anomaly detection. The shape of object motion trajectory is modeled using Kernel Density Estimation (KDE), making use of both the angle attribute of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, based on the Information Bottleneck (IB) method, is employed for trajectory learning to obtain an adaptive number of trajectory clusters through maximizing the Mutual Information (MI) between the clustering result and a feature set of the trajectory data. Furthermore, we propose to effectively enhance the performance of IB by taking into account the clustering quality in each iteration of the clustering procedure. The trajectories are determined as either abnormal (infrequently observed) or normal by a measure based on Shannon entropy. Extensive tests on real-world and synthetic data show that the proposed technique behaves very well and outperforms the state-of-the-art methods.

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Von Mises Mixture PHD Filter

Cited by 18 publications

References 28 publications

Tracking Multiple Audio Sources With the von Mises Distribution and Variational EM

Tracking Multiple Audio Sources With the von Mises Distribution and Variational EM

Mixture Reduction on Matrix Lie Groups

Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools

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