Guest Editors
Dr. Víctor García
Email: victga24@ucm.es
Affiliation: Universidad Complutense de Madrid (UCM), Madrid, Spain
Homepage:
Research Interests: AI, machine learning, computer vision
Summary
Object detection and multi-object tracking are core tasks in computer vision, underpinning applications such as autonomous driving, intelligent surveillance, and human–computer interaction. While deep learning has significantly advanced these fields, challenges such as occlusion, real-time processing, and domain adaptation remain critical barriers to practical deployment.
This Special Issue seeks high-quality contributions on state-of-the-art deep learning methods for object detection and multi-object tracking. Topics of interest include, but are not limited to:
- Novel deep learning architectures for object detection and tracking
- Real-time and low-latency detection and tracking systems
- Multi-object tracking in complex and dynamic environments
- Transformer-based and attention-based models for detection and tracking
- Domain adaptation and generalization in object detection and tracking
- 3D object detection and tracking (e.g., LiDAR, RGB-D data)
- Multi-modal approaches (e.g., vision + sensor fusion)
- Benchmarking, datasets, and evaluation metrics
- Applications in autonomous driving, surveillance, and robotics
- Explainability and interpretability in detection and tracking models
- Energy-efficient and edge-based deep learning solutions
- Robustness, fairness, and bias in detection and tracking systems
Keywords
computer vision, deep learning, object detection, multi-object tracking, real-time vision
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