Abstract: Real-Time Online Adaption for Robust Instrument Tracking and Pose Estimation

In [1], we propose a novel method for instrument tracking in Retinal Microsurgery (RM) which is apt to withstand the challenges of in-vivo RM visual sequences. The proposed approach is a robust closed-loop framework to track and localize the instrument parts in real-time, based on a dual-random forest (RF). First, a tracker employs the pixel intensities in a RF to infer the bounding box around the tool tip. In the second step, this region of interest is forwarded to another RF, which predicts the locations of the tool joints based on HOG features.