Robust Visual

Robust visual tracking aims to accurately and reliably follow objects in video sequences, overcoming challenges like occlusion, illumination changes, and fast motion. Current research emphasizes developing algorithms that leverage diverse data sources (e.g., LiDAR, RGB, event cameras) and incorporate advanced techniques such as deep Siamese networks, particle filters, and robust regression to improve tracking accuracy and efficiency. These advancements are crucial for applications ranging from autonomous driving and augmented reality to video surveillance and robotics, where reliable object tracking is essential for safe and effective operation.