@article{discovery10119196,
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          volume = {9},
          number = {3},
         journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
            year = {2021},
           title = {Towards real-time multiple surgical tool tracking},
           pages = {279--285},
        keywords = {Multiple tool tracking, Surgical data science, Computer-Assisted Interventions},
          author = {Robu, M and Kadkhodamohammadi, A and Luengo, I and Stoyanov, D},
        abstract = {Surgical tool tracking is an essential building block for computer-assisted interventions (CAI) and applications like video summarisation, workflow analysis and surgical navigation. Vision-based instrument tracking in laparoscopic surgical data faces significant challenges such as fast instrument motion, multiple simultaneous instruments and re-initialisation due to out-of-view conditions or instrument occlusions. In this paper, we propose a real-time multiple object tracking framework for whole laparoscopic tools, which extends an existing single object tracker. We introduce a geometric object descriptor, which helps with overlapping bounding box disambiguation, fast motion and optimal assignment between existing trajectories and new hypotheses. We achieve 99.51\% and 75.64\% average accuracy on ex-vivo robotic data and in-vivo laparoscopic sequences respectively from the Endovis'15 Instrument Tracking Dataset. The proposed geometric descriptor increased the performance on laparoscopic data by 32\%, significantly reducing identity switches, false negatives and false positives. Overall, the proposed pipeline can successfully recover trajectories over long-sequences and it runs in real-time at approximately 25-29 fps.},
             url = {https://doi.org/10.1080/21681163.2020.1835553}
}