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ESAD: Endoscopic Surgeon Action Detection dataset

Abstract

In this work, we take aim towards increasing the effectiveness of surgical assistant robots. We intended to make assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. In other words, we aim to solve the problem of surgeon action detection in endoscopic videos. To this, we introduce a challenging dataset for surgeon action detection in real world endoscopic videos. Action classes are picked based on the feedback of surgeons and annotated by medical professional. Given a video frame, we draw bounding box around surgical tool which is performing action and label it with action label. Finally, we present a frame-level action detection baseline model based on recent advances in object detection. Results on our new dataset show that our presented dataset provides enough interesting challenges for future method and it can serve as strong benchmark corresponding research in surgeon action detection in endoscopic videos.

DOI (Digital Object Identifier)

Permanent link to this resource: https://doi.org/10.48550/arXiv.2006.07164

Attached files

Authors

Singh Bawac, Vivek
Singh, Gurkit
Kaping’A, Francis
Skarga-Bandurova, Inna
Leporini, Alice
Landolfo, Carmela
Stabile, Armando
Setti, Franesco
Muradore, Riccardo
Oleari, Elettra
Cuzzolin, Fabio

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2022
Date of RADAR deposit: 2022-07-08



http://arxiv.org/licenses/nonexclusive-distrib/1.0/


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