Artificial Intelligence-Based Object Detection and Tracking: Theory and Applications

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1282

Special Issue Editors

Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China
Interests: computer vision; object tracking; machine learning; self-supervised learning; active learning
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Guest Editor
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Interests: computer vision; image processing; medical image segmentation; active learning

Special Issue Information

Dear Colleagues,

This Special Issue explores the symbiotic relationship between artificial intelligence (AI) and object tracking and detection technologies, delving into practical applications and theoretical foundations propelling innovation in computer vision. Object tracking and detection, driven by AI, are pivotal research domains aiming for automatic localization and recognition in images or videos. Applied in surveillance cameras, they enable real-time monitoring, security alerts, and behavior analysis for pedestrians, vehicles, and other objects. In autonomous driving, AI-based tracking and detection contribute to tasks like environment perception, detecting vehicles, pedestrians, and traffic lights. Facial recognition relies on AI-driven object detection and tracking for identity verification in access control and security monitoring. As AI-based object tracking and detection evolve, their applications expand across various domains. The technology’s attention stems from wide-ranging applications, showing progress in recent studies using multi-modal data for tracking and detection tasks. Deep learning algorithms for target tracking exhibit satisfactory performance, overcoming challenges like a shortage of labeled training data and model representation limitations. Current research in AI-based object tracking and detection showcases advancements and ongoing challenges, particularly in diverse application scenarios. The development of these technologies, driven by AI, remains crucial in computer vision. This Special Issue spotlights exceptional research in AI-driven object tracking and detection, emphasizing cutting-edge advances, developments, and emerging trends. We welcome high-quality papers addressing both theoretical and practical dimensions of AI-based object tracking and detection.

Dr. Di Yuan
Dr. Xiu Shu
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • computer vision
  • object tracking and detection systems
  • single/multiple object tracking
  • object detection and its applications
  • person re-ID and person search
  • self-supervised/unsupervised learning
  • image fusion and its applications
  • thermal infrared target tracking and detection
  • tiny/small target tracking and detection
  • deep learning for object tracking
  • object tracking and its applications
  • image restoration
  • object recognition

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Published Papers (1 paper)

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Research

44 pages, 38981 KiB  
Article
From Camera Image to Active Target Tracking: Modelling, Encoding and Metrical Analysis for Unmanned Underwater Vehicles
by Samuel Appleby, Giacomo Bergami and Gary Ushaw
AI 2025, 6(4), 71; https://doi.org/10.3390/ai6040071 - 7 Apr 2025
Viewed by 313
Abstract
Marine mammal monitoring, a growing field of research, is critical to cetacean conservation. Traditional ‘tagging’ attaches sensors such as GPS to such animals, though these are intrusive and susceptible to infection and, ultimately, death. A less intrusive approach exploits UUV commanded by a [...] Read more.
Marine mammal monitoring, a growing field of research, is critical to cetacean conservation. Traditional ‘tagging’ attaches sensors such as GPS to such animals, though these are intrusive and susceptible to infection and, ultimately, death. A less intrusive approach exploits UUV commanded by a human operator above ground. The development of AI for autonomous underwater vehicle navigation models training environments in simulation, providing visual and physical fidelity suitable for sim-to-real transfer. Previous solutions, including UVMS and L2D, provide only satisfactory results, due to poor environment generalisation while sensors including sonar create environmental disturbances. Though rich in features, image data suffer from high dimensionality, providing a state space too great for many machine learning tasks. Underwater environments, susceptible to image noise, further complicate this issue. We propose SWiMM2.0, coupling a Unity simulation modelling of a BLUEROV UUV with a DRL backend. A pre-processing step exploits a state-of-the-art CMVAE, reducing dimensionality while minimising data loss. Sim-to-real generalisation is validated by prior research. Custom behaviour metrics, unbiased to the naked eye and unprecedented in current ROV simulators, link our objectives ensuring successful ROV behaviour while tracking targets. Our experiments show that SAC maximises the former, achieving near-perfect behaviour while exploiting image data alone. Full article
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