  In this paper, we address the open research problem of surgical gesture
recognition using motion cues from video data only. We adapt Optical flow
ConvNets initially proposed by Simonyan et al.. While Simonyan uses both RGB
frames and dense optical flow, we use only dense optical flow representations
as input to emphasize the role of motion in surgical gesture recognition, and
present it as a robust alternative to kinematic data. We also overcome one of
the limitations of Optical flow ConvNets by initializing our model with cross
modality pre-training. A large number of promising studies that address
surgical gesture recognition highly rely on kinematic data which requires
additional recording devices. To our knowledge, this is the first paper that
addresses surgical gesture recognition using dense optical flow information
only. We achieve competitive results on JIGSAWS dataset, moreover, our model
achieves more robust results with less standard deviation, which suggests
optical flow information can be used as an alternative to kinematic data for
the recognition of surgical gestures.
