Paper Info
Title
Product of tracking experts for visual tracking of surgical tools
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.
Year
DOI
Venue
2013
10.1109/CoASE.2013.6654037
Automation Science and Engineering
Keywords
Field
DocType
end effectors,medical image processing,medical robotics,object detection,object tracking,robot vision,statistical analysis,surgery,video signal processing,PoTE based generalized object tracking framework,computer-aided surgical interventions,object detection methods,object detection-based tracking,point-feature correspondence,point-feature-based tracking,probabilistically-merging tracking outputs,region-based tracking,surgical robotics,surgical tool end-effector,surgical tools,tool detection approach,tool localization,tool shape,uncalibrated monocular surgical videos,visual tracking,voting based bounding box geometry estimation technique
BitTorrent tracker,Computer vision,Object detection,Computer science,Tracking system,Robot end effector,Video tracking,Eye tracking,Artificial intelligence,Probabilistic logic,Minimum bounding box
Conference
ISSN
Citations 
PageRank 
2161-8070
7
0.48
References 
Authors
13
4
Name
Order
Citations
PageRank
Subodh Kumar152749.65
Narayanan, M.S.290.89
Pankaj Singhal3111.53
Corso Jason J.4144292.44