Tracking Model

Visual tracking models aim to automatically follow objects across video frames or images, a crucial task in computer vision with applications ranging from autonomous driving to medical image analysis. Current research emphasizes developing efficient and robust trackers, focusing on lightweight architectures like hierarchical transformers and pruned convolutional neural networks to improve speed and resource usage on constrained devices. Furthermore, research explores advanced training strategies, such as sequence-level learning and online learning methods, to enhance accuracy and adaptability, particularly for unified segmentation and tracking approaches. These advancements are driving significant improvements in tracking performance across various benchmarks and domains.