Matteo Dunnhofer

TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.

TL;DR: This study proposes a new deep learning method to track, accurately and efficiently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups.

TL;DR: The proposed UNet has the potential to localise femoral cartilage in robotic knee arthroscopy with clinical accuracy and a novel metric concept named Dice coefficient with boundary uncertainty (DSCUB) was proposed and used to test the algorithm.