Product of tracking experts for visual tracking of surgical tools

Suren Kumar, Madusudanan Sathia Narayanan, Pankaj Singhal, Jason J. Corso, Venkat Krovi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

This paper proposes a novel tool detection and tracking approach using uncalibrated monocular surgical videos for computer-aided surgical interventions. We hypothesize surgical tool end-effector to be the most distinguishable part of a tool and employ state-of-the-art object detection methods to learn the shape and localize the tool in images. For tracking, we propose a Product of Tracking Experts (PoTE) based generalized object tracking framework by probabilistically-merging tracking outputs (probabilistic/non- probabilistic) from time-varying numbers of trackers. In the current implementation of PoTE, we use three tracking experts - point-feature-based, region-based and object detection-based. A novel point feature-based tracker is also proposed in the form of a voting based bounding box geometry estimation technique building upon point-feature correspondences. Our tracker is causal which makes it suitable for real-time applications. This framework has been tested on real surgical videos and is shown to significantly improve upon the baseline results.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
Pages480-485
Number of pages6
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Automation Science and Engineering, CASE 2013 - Madison, WI, United States
Duration: 17 Aug 201320 Aug 2013

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
Country/TerritoryUnited States
CityMadison, WI
Period17/08/1320/08/13

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