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Computation
Special Issues
Moving Object Detection Using Computational Methods and Modeling
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Moving Object Detection Using Computational Methods and Modeling

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 780

Special Issue Editors

Dr. Sergii Khlamov

E-Mail Website
Guest Editor
Department of Media Systems and Technologies, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
Interests: computational methods; mathematical modeling (statistical and in situ); image recognition; big data and data science; data mining; observational astronomy
Dr. Oleg Sergiyenko

E-Mail Website
Guest Editor
Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali B.C. 21100, Mexico
Interests: automated metrology; 3D coordinate measurement; robotic navigation; machine vision; simulation of robotic swarm behavior
Special Issues, Collections and Topics in MDPI journals
Dr. David E Mkrtichian

E-Mail Website
Guest Editor
National Astronomical Research Institute of Thailand (NARIT), Chiangmai 50180, Thailand
Interests: astronomical instrumentation; methods and techniques; observational methods; machine vision; motion detection; high-resolution spectroscopy; astrometry; photometry; asteroseismology; variable stars

Special Issue Information

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue titled “Moving Object Detection Using Computational Methods and Modeling”. This Special Issue aims to gather research studies across various disciplines that shed light on the cutting-edge uses of computational techniques and methods in the field of motion detection and tracking.

This Special Issue focuses on advancements in detecting and tracking moving objects using innovative computational approaches and mathematical modeling. It aims to showcase research on modern techniques, including classical computational methods, artificial intelligence (AI), machine learning and sensor integration, that address challenges in dynamic environments. The topics in this Special Issue will explore applications across the different domains, highlighting both theoretical developments and practical implementations. Some of these domains include robotics, autonomous systems, aerospace, drones, maritime and underwater applications, surveillance, defense, traffic monitoring, healthcare, astronomy and others, where we can detect moving objects in the field of view of a charge-coupled device (CCD) camera.

The articles collected in this Special Issue will cover a broad spectrum of topics, including, but not limited to, image processing, machine vision, image and pattern recognition, AI-enhanced predictive modeling, big data analysis, and machine and deep learning. With this Special Issue, we aim to provide a comprehensive overview of the current state of the art of this field and to inspire innovative future research.

This Special Issue is a call to all researchers, data scientists, scholars and engineers to submit their original research, reviews, case studies and thought-provoking perspectives that demonstrate the novel uses and potential applications of computational methods in the field of motion detection and tracking.

Dr. Sergii Khlamov
Dr. Oleg Sergiyenko
Dr. David E Mkrtichian
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. Computation 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

  • computational methods
  • motion detection
  • object tracking
  • mathematical modeling
  • machine vision
  • image and pattern recognition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

28 pages, 8982 KiB  
Article
Decision-Level Multi-Sensor Fusion to Improve Limitations of Single-Camera-Based CNN Classification in Precision Farming: Application in Weed Detection
by Md. Nazmuzzaman Khan, Adibuzzaman Rahi, Mohammad Al Hasan and Sohel Anwar
Computation 2025, 13(7), 174; https://doi.org/10.3390/computation13070174 - 18 Jul 2025
Viewed by 366
Abstract
The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in [...] Read more.
The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article
(This article belongs to the Special Issue Moving Object Detection Using Computational Methods and Modeling)
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