Robust Tracking

Robust tracking aims to reliably identify and follow objects across video sequences, even under challenging conditions like occlusion, rapid motion, or adverse weather. Current research emphasizes improving tracking robustness through advanced model architectures, such as transformers and Siamese networks, often combined with innovative algorithms like particle filters and Kalman filters, and incorporating techniques like prompt engineering and adversarial training. These advancements are crucial for diverse applications, including autonomous driving, robotics, surveillance, and precision agriculture, where reliable object tracking is essential for safe and efficient operation.