Special Issues
Table of Content

Secure & Intelligent Cloud-Edge Systems for Real-Time Object Detection and Tracking

Submission Deadline: 31 December 2025 View: 533 Submit to Special Issue

Guest Editors

Assoc. Prof. Said Jabbour

Email: jabbour@cril.fr

Affiliation: Lens Computer Research Center (CRIL), Artois University, Lens, 62300, France

Homepage:

Research Interests: explainable AI, pattern mining, knowledge representa tion, satisfiability

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Assoc. Prof. Azidine Guezzaz

Email: a.guezzaz@uca.ma

Affiliation: Computer Science and Mathematics, Cadi Ayyad University Marrakech, Essaouira, 44000, Morocco

Homepage:

Research Interests: computer security, cryptography, artificial intelligence, intrusion detection and healthcare systems

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Prof. Dr. Mourade Azrour

Email: mo.azrour@umi.ac.ma

Affiliation: Faculty of Sciences and Technics, Moulay Ismail University, Meknes, 50000, Morocco

Homepage:

Research Interests: authentication protocol, computer networks and computer security, and reliability

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Summary

Traditional centralized architectures are inadequate for meeting the stringent requirements of real-time object detection and tracking, particularly in terms of latency, scalability, and security. Integrating cloud and edge computing addresses these limitations by distributing computational tasks closer to the data source, thereby reducing latency and enabling faster, context-aware decision-making.

This special issue focuses on the convergence of cloud/ edge computing intelligence to design secure and intelligent systems for real-time object detection and tracking. It seeks original contributions that present novel architectures, theoretical advancements, and practical implementations enabling secure and efficient coordination of distributed resources and intelligent agents.

Suggested Topics but not limited to:
· Cloud-Edge Synergy for Low-Latency Object Detection
· Edge/cloud-assisted object detection and tracking frameworks
· Edge Intelligence for Context-Aware and Real-Time Decision Making
· Privacy-preserving computing in edge/cloud environments
· Intelligent resource allocation in multi-agent edge computing


Keywords

Cloud-Edge Computing, Real-Time Object Detection, Edge Intelligence, Low Latency, Distributed Systems, Scalability and Security, Intelligent Resource Allocation

Published Papers


  • Open Access

    ARTICLE

    Face-Pedestrian Joint Feature Modeling with Cross-Category Dynamic Matching for Occlusion-Robust Multi-Object Tracking

    Qin Hu, Hongshan Kong
    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.069078
    (This article belongs to the Special Issue: Secure & Intelligent Cloud-Edge Systems for Real-Time Object Detection and Tracking)
    Abstract To address the issues of frequent identity switches (IDs) and degraded identification accuracy in multi object tracking (MOT) under complex occlusion scenarios, this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling. By constructing a joint tracking model centered on “intra-class independent tracking + cross-category dynamic binding”, designing a multi-modal matching metric with spatio-temporal and appearance constraints, and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy, this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion, cross-camera tracking, and crowded environments. Experiments… More >

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