TY - GEN
T1 - Product of tracking experts for visual tracking of surgical tools
AU - Kumar, Suren
AU - Narayanan, Madusudanan Sathia
AU - Singhal, Pankaj
AU - Corso, Jason J.
AU - Krovi, Venkat
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84891510052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84891510052&partnerID=8YFLogxK
U2 - 10.1109/CoASE.2013.6654037
DO - 10.1109/CoASE.2013.6654037
M3 - Conference contribution
AN - SCOPUS:84891510052
SN - 9781479915156
T3 - IEEE International Conference on Automation Science and Engineering
SP - 480
EP - 485
BT - 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
T2 - 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
Y2 - 17 August 2013 through 20 August 2013
ER -