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Special Issue "Multiple Object Tracking: Making Sense of the Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 15 February 2019

Special Issue Editor

Guest Editor
Prof. Dr. Ba-Ngu Vo

Department of Electrical & Computer Engineering, Curtin University, Perth, Australia
Website | E-Mail
Interests: multi-target tracking; multi-object tracking; random set; finite set statistics; point process; filtering; Bayesian methods; probability hypothesis density; data fusion; sensor management

Special Issue Information

Dear Colleagues,

Advances in sensing technology and the proliferation of sensors have been the main drivers for automated recognition and interpretation of object motion from sensor data. Making sense of sensor data is an important objective for multiple-object tracking, and is an essential task in many applications, including surveillance, oceanography, autonomous vehicles, computer vision, remote sensing, biomedical research, and so on. The last decade has witnessed many advances in the field, both in terms of theory and applications.

This call for papers invites technical contributions to Sensors Special Issue on “Multiple Object Tracking: Making Sense of the Sensors”. The Special Issue aims to provide an up-to-date overview of multiple object tracking theory and solutions, as well as a forum for sharing innovative applications. Potential topics include, but are not limited to:

  • Multiple object tracking algorithms
  • Multiple object system models
  • Metrics and performance evaluation for multiple object tracking
  • Track before detect
  • Sensor management for multiple object tracking
  • Tracking with unknown system parameters
  • Multiple object system identification
  • Distributed multiple object tracking

Prof. Dr. Ba-Ngu Vo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multiple Object tracking
  • Multiple Target Tracking
  • Non-linear Filtering
  • Bayesian estimation
  • Data Fusion
  • Sensor Management
  • Systems Theory

Published Papers (3 papers)

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Research

Open AccessArticle Target Localization and Tracking by Fusing Doppler Differentials from Cellular Emanations with a Multi-Spectral Video Tracker
Sensors 2018, 18(11), 3687; https://doi.org/10.3390/s18113687
Received: 10 October 2018 / Accepted: 25 October 2018 / Published: 30 October 2018
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Abstract
We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused
[...] Read more.
We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed from the multi-spectral tracker output. The absolute difference and the root-mean-square difference are computed to associate the Doppler differentials from the two sensor systems. Performance of the algorithm was evaluated using synthetically generated datasets of an urban scene with multiple moving vehicles. The presented fusion algorithm correctly associates the cellular emanation with the corresponding video target for low measurement uncertainty and in the presence of favorable motion patterns. For nearly all objects the fusion algorithm has high confidence in associating the emanation with the correct multi-spectral target from the most probable background target. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
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Open AccessArticle Joint Detection and DOA Tracking with a Bernoulli Filter Based on Information Theoretic Criteria
Sensors 2018, 18(10), 3473; https://doi.org/10.3390/s18103473
Received: 11 September 2018 / Revised: 12 October 2018 / Accepted: 14 October 2018 / Published: 15 October 2018
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Abstract
In this paper, we study the problem of the joint detection and direction-of-arrival (DOA) tracking of a single moving source which can randomly appear or disappear from the surveillance volume. Firstly, the Bernoulli random finite set (RFS) is employed to characterize the randomness
[...] Read more.
In this paper, we study the problem of the joint detection and direction-of-arrival (DOA) tracking of a single moving source which can randomly appear or disappear from the surveillance volume. Firstly, the Bernoulli random finite set (RFS) is employed to characterize the randomness of the state process, i.e., the dynamics of the source motion and the source appearance. To increase the performance of the detection and DOA tracking in low signal-to-noise ratio (SNR) scenarios, the measurements are obtained directly from an array of sensors and allow multiple snapshots. A track-before-detect (TBD) Bernoulli filter is proposed for tracking a randomly on/off switching single dynamic system. Secondly, since the variances of the stochastic signal and measurement noise are unknown in practical applications, these nuisance parameters are marginalized by defining an uninformative prior, and the likelihood function is compensated by using the information theoretic criteria. The simulation results demonstrate the performance of the filter. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

Open AccessArticle Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering
Sensors 2018, 18(9), 3005; https://doi.org/10.3390/s18093005
Received: 25 July 2018 / Revised: 3 September 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
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Abstract
Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized δ-generalized labeled multi-Bernoulli filtering on a network of nodes consisting of
[...] Read more.
Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized δ -generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

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