Object Recognition and Target Detection in Computer Vision
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 2
Special Issue Editor
Special Issue Information
Dear Colleagues,
Object recognition and target detection are fundamental components of computer vision, enabling machines to intelligently analyze and interact with visual environments. These tasks are essential for a wide range of applications, including pedestrian and obstacle detection in autonomous vehicles, tumor localization in medical imaging, and anomaly detection and activity monitoring in surveillance systems.
Object detection has evolved from early template-matching and feature-based methods, such as the Viola–Jones detector (which enabled real-time face detection), to more advanced handcrafted descriptors like SIFT and HOG for improved object localization. The rise of deep learning introduced CNN-based models, including variants of R-CNN, transforming the field. The development of YOLO and SSD prioritized real-time performance, while transformer-based models enhanced accuracy and robustness in complex scenarios. Recent advancements in deep learning, particularly transformer-based architectures (e.g., Vision Transformers, DETR) and hybrid AI approaches (e.g., neuro-symbolic systems, multimodal fusion), have significantly improved accuracy, real-time processing, and robustness. However, challenges such as occlusion handling, scalability in low-resource environments, 3D perception, and ethical concerns—including bias mitigation and privacy-aware tracking—remain open research problems.
This Special Issue aims to highlight innovative research directions to address these challenges, emphasizing novel algorithms, datasets, and applications. We seek to present this Special Issue as an interdisciplinary collaboration and drive advancements toward reliable, deployable vision systems. Therefore, we invite authors to submit original and unpublished results on topics including, but not limited to, efficient deep learning models, explainable AI for object detection, and emerging trends such as event-based vision and embodied AI.
Dr. Bushra Jalil
Guest Editor
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Keywords
- real-time object detection and tracking
- deep learning-based tracking
- transformer models for tracking
- multi object and target detection
- 3D object detection and tracking
- edge AI for object tracking
- cross-domain object tracking
- applications in medical imaging
- remote sensing
- autonomous systems
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