Instance Tracking

Instance tracking, a core computer vision problem, aims to identify and follow individual objects across video frames. Current research emphasizes improving tracking accuracy and efficiency, particularly in challenging scenarios like occlusions and fast motion, using techniques such as dynamic transformer networks and context-aware approaches that leverage surrounding information. This focus is driven by the need for robust tracking in diverse applications, from augmented reality and autonomous driving to video analysis and human-computer interaction, with recent work highlighting the importance of developing more human-like tracking capabilities and standardized evaluation benchmarks.