E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

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 semimonthly 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 (9 papers)

View options order results:
result details:
Displaying articles 1-9
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle
Sensors 2019, 19(2), 391; https://doi.org/10.3390/s19020391
Received: 15 December 2018 / Revised: 4 January 2019 / Accepted: 15 January 2019 / Published: 18 January 2019
PDF Full-text (20190 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we present a novel 2D–3D pedestrian tracker designed for applications in autonomous vehicles. The system operates on a tracking by detection principle and can track multiple pedestrians in complex urban traffic situations. By using a behavioral motion model and a
[...] Read more.
In this paper, we present a novel 2D–3D pedestrian tracker designed for applications in autonomous vehicles. The system operates on a tracking by detection principle and can track multiple pedestrians in complex urban traffic situations. By using a behavioral motion model and a non-parametric distribution as state model, we are able to accurately track unpredictable pedestrian motion in the presence of heavy occlusion. Tracking is performed independently, on the image and ground plane, in global, motion compensated coordinates. We employ Camera and LiDAR data fusion to solve the association problem where the optimal solution is found by matching 2D and 3D detections to tracks using a joint log-likelihood observation model. Each 2D–3D particle filter then updates their state from associated observations and a behavioral motion model. Each particle moves independently following the pedestrian motion parameters which we learned offline from an annotated training dataset. Temporal stability of the state variables is achieved by modeling each track as a Markov Decision Process with probabilistic state transition properties. A novel track management system then handles high level actions such as track creation, deletion and interaction. Using a probabilistic track score the track manager can cull false and ambiguous detections while updating tracks with detections from actual pedestrians. Our system is implemented on a GPU and exploits the massively parallelizable nature of particle filters. Due to the Markovian nature of our track representation, the system achieves real-time performance operating with a minimal memory footprint. Exhaustive and independent evaluation of our tracker was performed by the KITTI benchmark server, where it was tested against a wide variety of unknown pedestrian tracking situations. On this realistic benchmark, we outperform all published pedestrian trackers in a multitude of tracking metrics. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

Open AccessArticle Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association
Sensors 2019, 19(1), 112; https://doi.org/10.3390/s19010112
Received: 14 November 2018 / Revised: 22 December 2018 / Accepted: 26 December 2018 / Published: 30 December 2018
PDF Full-text (390 KB) | HTML Full-text | XML Full-text
Abstract
In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data
[...] Read more.
In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

Open AccessArticle Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors
Sensors 2019, 19(1), 3; https://doi.org/10.3390/s19010003
Received: 4 December 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
PDF Full-text (1838 KB) | HTML Full-text | XML Full-text
Abstract
As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel
[...] Read more.
As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel fault diagnosis method for train plug doors using multi-scale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class support vector machine (IPSO-MSVM) is proposed. Firstly, sound samples are collected using high-precision audio sensor. In the features extraction process, a hybrid method blending empirical mode decomposition (EMD), multi-scale permutation entropy (MNPE) with Fisher discrimination criterion is utilized. First, EMD is used to decompose each sound signal into several intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. To obtain the most significant features, the Fisher discrimination criterion is further applied. To address the time-consuming defects of traditional grid based method for selecting the optimal parameters of multi-class SVM, an improved PSO (IPSO) is proposed. The superiority of the IPSO-MSVM model and the hybrid feature extraction method was tested on the collected sound samples by comparing to commonly applied methods. Results indicate the identification accuracy of the proposed method is highest, which reaches 90.54%, demonstrating its feasibility. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

Open AccessArticle Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking
Sensors 2018, 18(12), 4416; https://doi.org/10.3390/s18124416
Received: 15 November 2018 / Revised: 6 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
PDF Full-text (1707 KB) | HTML Full-text | XML Full-text
Abstract
The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching
[...] Read more.
The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

Open AccessArticle Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network
Sensors 2018, 18(12), 4115; https://doi.org/10.3390/s18124115
Received: 11 October 2018 / Revised: 20 November 2018 / Accepted: 20 November 2018 / Published: 23 November 2018
PDF Full-text (3171 KB) | HTML Full-text | XML Full-text
Abstract
A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted
[...] Read more.
A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Figures

Figure 1

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
PDF Full-text (2282 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

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
PDF Full-text (6677 KB) | HTML Full-text | XML Full-text
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
Cited by 1 | PDF Full-text (1795 KB) | HTML Full-text | XML Full-text
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

Review

Jump to: Research

Open AccessReview “Statistics 103” for Multitarget Tracking
Sensors 2019, 19(1), 202; https://doi.org/10.3390/s19010202
Received: 3 December 2018 / Revised: 24 December 2018 / Accepted: 24 December 2018 / Published: 8 January 2019
PDF Full-text (379 KB) | HTML Full-text | XML Full-text
Abstract
The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and
[...] Read more.
The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top