Deep Learning Techniques for Object Detection and Tracking

Dear Colleagues,

This Special Issue aims to explore recent advancements in deep learning techniques for object detection and tracking, focusing on both fundamental algorithmic developments and real-world applications across diverse domains such as autonomous systems, surveillance, robotics, and medical imaging.

A particular emphasis is placed on the integration of Large Language Models (LLMs) and foundation models for Vision–Language Models (VLMs), which are reshaping the landscape of multimodal perception. These powerful architectures enable context-aware object understanding and open-ended visual question answering, bridging the gap between semantic language descriptions and visual detection tasks. Contributions that investigate prompt-based visual tracking, cross-modal learning, and zero-shot detection using pre-trained foundation models are highly encouraged.

We welcome original research and review articles on topics including, but not limited to, the following:

• Deep neural networks for object detection and multi-object tracking;
• Transformer-based and attention-driven detection frameworks;
• Real-time tracking and re-identification;
• Multimodal detection using VLMs and LLM-guided perception;
• Self-supervised and unsupervised learning for detection;
• Transfer learning and domain adaptation in tracking systems;
• Applications of foundation models (e.g., CLIP, DINO, SAM, GPT-V) in vision–language tasks.

This Special Issue seeks to provide a timely platform for showcasing innovative solutions that push the boundaries of intelligent visual perception.

Prof. Dr. Byung-Gyu Kim
Guest Editor

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