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Unified Detection and Tracking of Instruments during Retinal Microsurgery
May 2013 (vol. 35 no. 5)
pp. 1263-1273
R. Sznitman, EPFL IC ISIM CVLAB, Lausanne, Switzerland
R. Richa, Johns Hopkins Univ., Baltimore, MD, USA
R. H. Taylor, Johns Hopkins Univ., Baltimore, MD, USA
B. Jedynak, Johns Hopkins Univ., Baltimore, MD, USA
G. D. Hager, Johns Hopkins Univ., Baltimore, MD, USA
Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.
Index Terms:
time series,Bayes methods,biomedical equipment,computer vision,entropy,eye,filtering theory,image motion analysis,medical computing,object detection,object tracking,optimisation,surgery,robust tracking,instrument detection,instrument tracking,retinal microsurgery,object tracking,local optimization,detection-based tracking,target appearance,target disappearance,target motion continuity,tracking initialization,time-series Bayesian estimation problem,sequential entropy minimization problem,active testing paradigm,Bayesian filtering,field of view,retinal tool tracking problem,Instruments,Target tracking,Surgery,Testing,Optimization,Aerospace electronics,retinal microsurgery,Unified object detection and tracking,active testing,instrument tracking,adaptive sensing
R. Sznitman, R. Richa, R. H. Taylor, B. Jedynak, G. D. Hager, "Unified Detection and Tracking of Instruments during Retinal Microsurgery," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1263-1273, May 2013, doi:10.1109/TPAMI.2012.209
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