Web Tracking

Web tracking encompasses the automated monitoring and analysis of objects or entities within a visual field, aiming for accurate localization, identification, and trajectory prediction. Current research emphasizes robust tracking across diverse conditions (e.g., adverse weather, occlusions, cluttered scenes) using various techniques, including deep learning models (e.g., transformers, U-Nets), Kalman filters, and graph-based methods, often integrated with sensor fusion (e.g., LiDAR, cameras, inertial sensors). These advancements have significant implications for numerous applications, including autonomous navigation, medical imaging, space situational awareness, and sports analytics, by improving the reliability and efficiency of object tracking systems.