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J. Imaging
Special Issues
Multi-Object Tracking
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Multi-Object Tracking

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 6876

Special Issue Editors

Prof. Dr. Phill Kyu Rhee

E-Mail Website
Guest Editor
Department of Computer Science & Engineering, Inha University, Incheon 402-751, Republic of Korea
Interests: computer vision; machine learning; object detection; object tracking; active learning; semi-supervised learning
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Howard

E-Mail Website
Guest Editor
1. Director, Howard Science Limited, Worcestershire WR14 2NJ, UK
2. Former Company Fellow and Capability Leader in Machine Vision, QinetiQ Group PLC/DERA, Malvern WR14 3PS, UK
3. Former Fellow and Current SCR Member, Pembroke College, University of Oxford, Oxford OX3 7LF, UK
Interests: image processing; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past three decades, the computer vision community has developed several methods, such as multi-object tracking (MOT), whose many challenges stem from the rapid expansion of its application areas in video surveillance, robotic vision, autonomous vehicles and object-of-interest tracking, indoor navigation, smart airport security, unmanned stores, etc. MOT is used to address illumination variations, fast motion, tracking performance, as well as challenges in tracking with occlusions between objects. MOT is always seeking quasi-optimized solutions as well as higher accuracy in this continuously expanding area. Practical technical methods as well as theories regarding the tracking of objects, and promising directions for success for this area of research are the subjects of this Special Issue on MOT techniques and applications.

Prof. Dr. Phill Kyu Rhee
Dr. Daniel Howard
Guest Editors

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 submissions that pass pre-check are 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. Journal of Imaging 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

  • multi-object tracking (MOT)
  • deep learning
  • machine learning
  • generative learning
  • discriminative learning
  • reinforcement learning
  • online deep learning
  • real-time MOT
  • feature-based MOT
  • segmentation-based MOT
  • estimation-based MOT
  • learning-based MOT
  • video surveillance
  • robotic vision
  • autonomous vehicle tracking
  • indoor navigation
  • smart airport security
  • unmanned store
  • Bayesian, heuristic, bioinspired, or any techniques that can achieve MOT

Published Papers (2 papers)

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Research

19 pages, 10222 KiB  
Article
Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
by Xiaohui Huang, Pan He, Anand Rangarajan and Sanjay Ranka
J. Imaging 2022, 8(4), 101; https://doi.org/10.3390/jimaging8040101 - 6 Apr 2022
Cited by 3 | Viewed by 3093
Abstract
Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, [...] Read more.
Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method. Full article
(This article belongs to the Special Issue Multi-Object Tracking)
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15 pages, 7028 KiB  
Article
Multiple Aerial Targets Re-Identification by 2D- and 3D- Kinematics-Based Matching
by Shao Xuan Seah, Yan Han Lau and Sutthiphong Srigrarom
J. Imaging 2022, 8(2), 26; https://doi.org/10.3390/jimaging8020026 - 28 Jan 2022
Cited by 5 | Viewed by 2983
Abstract
This paper presents two techniques in the matching and re-identification of multiple aerial target detections from multiple electro-optical devices: 2-dimensional and 3-dimensional kinematics-based matching. The main advantage of these methods over traditional image-based methods is that no prior image-based training is required; instead, [...] Read more.
This paper presents two techniques in the matching and re-identification of multiple aerial target detections from multiple electro-optical devices: 2-dimensional and 3-dimensional kinematics-based matching. The main advantage of these methods over traditional image-based methods is that no prior image-based training is required; instead, relatively simpler graph matching algorithms are used. The first 2-dimensional method relies solely on the kinematic and geometric projections of the detected targets onto the images captured by the various cameras. Matching and re-identification across frames were performed using a series of correlation-based methods. This method is suitable for all targets with distinct motion observed by the camera. The second 3-dimensional method relies on the change in the size of detected targets to estimate motion in the focal axis by constructing an instantaneous direction vector in 3D space that is independent of camera pose. Matching and re-identification were achieved by directly comparing these vectors across frames under a global coordinate system. Such a method is suitable for targets in near to medium range where changes in detection sizes may be observed. While no overlapping field of view requirements were explicitly imposed, it is necessary for the aerial target to be detected in both cameras before matching can be carried out. Preliminary flight tests were conducted using 2–3 drones at varying ranges, and the effectiveness of these techniques was tested and compared. Using these proposed techniques, an MOTA score of more than 80% was achieved. Full article
(This article belongs to the Special Issue Multi-Object Tracking)
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