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

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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