Planar Object Tracking

Planar object tracking focuses on accurately estimating the pose of a flat object in a video sequence, a crucial task in robotics and augmented reality. Recent research emphasizes developing robust and accurate tracking algorithms, often employing deep learning models that leverage optical flow, homography estimation, and confidence measures to handle challenging scenarios like rapid motion, occlusions, and appearance changes. These advancements are driven by the need for larger, more realistic datasets and are leading to improved performance on benchmark evaluations, ultimately enhancing the reliability of applications requiring precise planar object localization.