Surgical tool tracking by on-line selection of structural correlation filters

Abstract

In visual tracking of surgical instruments, correlation filtering finds the best candidate with maximal correlation peak. However, most trackers only consider capturing target appearance but not target structure. In this paper we propose surgical instrument tracking approach that integrates prior knowledge related to rotation of both shaft and tool tips. To this end, we employ rigid parts mixtures model of an instrument. The rigidly composed parts encode diverse, pose-specific appearance mixtures of the tool. Tracking search space is confined to the neighbourhood of tool position, scale, and rotation with respect to previous best estimate such that the rotation constraint translates into querying subset of templates. Qualitative and quantitative evaluation on challenging benchmarks demonstrate state-of-the-art results.