Explore object tracking in computer vision through this comprehensive lecture by Arnold W.M. Smeulders from the University of Amsterdam. Delve into the challenges and recent developments in tracking algorithms, including insights from an international experimental review. Learn about various tracking methods such as Normalized Cross-Correlation, Lucas Kanade Tracker, Mean Shift Tracker, and Incremental Visual Tracking. Examine the impact of shadows, feature types, and updating strategies on tracking performance. Gain a systematic overview of trackers, understanding their spatial representation, appearance representation, motion models, inference methods, and internal model updates. Analyze the hardness of tracking and the complexities involved in learning targets from dynamic scenes with unpredicted motion.