ESAD: Endoscopic Surgeon Action Detection Dataset
This work addresses safety in surgical robotics by enabling robots to detect surgeon actions, though it is incremental as it focuses on dataset creation and baseline methods.
The authors tackled the problem of surgeon action detection in endoscopic videos to improve surgical assistant robot safety by introducing a new dataset with bounding box annotations and action labels, and presented a baseline model showing the dataset provides challenging benchmarks for future research.
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 presenta frame-level action detection baseline model based on recent advances in ob-ject detection. Results on our new dataset show that our presented dataset provides enough interesting challenges for future method and it can serveas strong benchmark corresponding research in surgeon action detection in endoscopic videos.