Unified detection and tracking of instruments during retinal microsurgery

Raphael Sznitman, Rogerio Richa, Russell H. Taylor, Bruno Jedynak, Gregory D. Hager

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number6319313
Pages (from-to)1263-1273
Number of pages11
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume35
Issue number5
DOIs
StatePublished - 2013

Keywords

  • Unified object detection and tracking
  • active testing
  • adaptive sensing
  • instrument tracking
  • retinal microsurgery

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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