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Symmetry
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Symmetry and Asymmetry in Object Detection, Object Tracking, and Behaviour...
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Symmetry and Asymmetry in Object Detection, Object Tracking, and Behaviour Understanding

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 1095

Special Issue Editor

Dr. Mohamed Naiel

E-Mail Website
Guest Editor
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
Interests: object detection; image segmentation; scene text recognition; object tracking

Special Issue Information

Dear Colleagues,

We are excited to invite you to contribute to a Special Issue on “Symmetry and Asymmetry in Object Detection, Object Tracking, and Behaviour Understanding”. The last decade has witnessed rapid advancements in computer vision and deep learning that have revolutionized how machines learn from visual information. Object detection, tracking, and behaviour understanding are pivotal for numerous video analytics applications, including autonomous driving, human–computer interaction, robotics, healthcare, agriculture, animal health, sports, surveillance, and traffic videos. This Special Issue aims to explore the latest developments, challenges, and opportunities in these areas, leveraging symmetry and/or asymmetry principals.

Dr. Mohamed Naiel
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 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 250 words) can be sent to the Editorial Office for assessment.

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. Symmetry 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 2400 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

  • object detection
  • pose estimation
  • multi-object tracking
  • object tracking
  • behaviour recognition
  • action recognition
  • activity 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

23 pages, 1945 KB  
Article
A Symmetry-Informed Multimodal LLM-Driven Approach to Robotic Object Manipulation: Lowering Entry Barriers in Mechatronics Education
by Jorge Gudiño-Lau, Miguel Durán-Fonseca, Luis E. Anido-Rifón and Pedro C. Santana-Mancilla
Symmetry 2025, 17(10), 1756; https://doi.org/10.3390/sym17101756 - 17 Oct 2025
Viewed by 700
Abstract
The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise physical actions. This paper presents a novel architecture for vision-based object manipulation that leverages [...] Read more.
The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise physical actions. This paper presents a novel architecture for vision-based object manipulation that leverages a VLM’s reasoning capabilities while incorporating symmetry principles to enhance operational efficiency. Implemented on a Yahboom DOFBOT educational robot with a Jetson Nano platform, our system introduces a prompt-based framework that uniquely embeds symmetry-related cues to streamline feature extraction and object detection from visual data. This methodology, which utilizes few-shot learning, enables the VLM to generate more accurate and contextually relevant commands for manipulation tasks by efficiently interpreting the symmetric and asymmetric features of objects. The experimental results in controlled scenarios demonstrate that our symmetry-informed approach significantly improves the robot’s interaction efficiency and decision-making accuracy compared to generic prompting strategies. This work contributes a robust method for integrating fundamental vision principles into modern generative AI workflows for robotics. Furthermore, its implementation on an accessible educational platform shows its potential to simplify complex robotics concepts for engineering education and research. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Object Detection, Object Tracking, and Behaviour Understanding)
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