  Accurate tool tracking is essential for the success of computer-assisted
intervention. Previous efforts often modeled tool trajectories rigidly,
overlooking the dynamic nature of surgical procedures, especially tracking
scenarios like out-of-body and out-of-camera views. Addressing this limitation,
the new CholecTrack20 dataset provides detailed labels that account for
multiple tool trajectories in three perspectives: (1) intraoperative, (2)
intracorporeal, and (3) visibility, representing the different types of
temporal duration of tool tracks. These fine-grained labels enhance tracking
flexibility but also increase the task complexity. Re-identifying tools after
occlusion or re-insertion into the body remains challenging due to high visual
similarity, especially among tools of the same category. This work recognizes
the critical role of the tool operators in distinguishing tool track instances,
especially those belonging to the same tool category. The operators'
information are however not explicitly captured in surgical videos. We
therefore propose SurgiTrack, a novel deep learning method that leverages
YOLOv7 for precise tool detection and employs an attention mechanism to model
the originating direction of the tools, as a proxy to their operators, for tool
re-identification. To handle diverse tool trajectory perspectives, SurgiTrack
employs a harmonizing bipartite matching graph, minimizing conflicts and
ensuring accurate tool identity association. Experimental results on
CholecTrack20 demonstrate SurgiTrack's effectiveness, outperforming baselines
and state-of-the-art methods with real-time inference capability. This work
sets a new standard in surgical tool tracking, providing dynamic trajectories
for more adaptable and precise assistance in minimally invasive surgeries.
