Density-Guided Label Smoothing for Temporal Localization of Driving
  Actions
Tunc Alkanat Erkut Akdag Egor Bondarev Peter H. N. De With
Temporal localization of driving actions plays a crucial role in advanced
driver-assistance systems and naturalistic driving studies. However, this is a
challenging task due to strict requirements for robustness, reliability and
accurate localization. In this work, we focus on improving the overall
performance by efficiently utilizing video action recognition networks and
adapting these to the problem of action localization. To this end, we first
develop a density-guided label smoothing technique based on label probability
distributions to facilitate better learning from boundary video-segments that
typically include multiple labels. Second, we design a post-processing step to
efficiently fuse information from video-segments and multiple camera views into
scene-level predictions, which facilitates elimination of false positives. Our
methodology yields a competitive performance on the A2 test set of the
naturalistic driving action recognition track of the 2022 NVIDIA AI City
Challenge with an F1 score of 0.271.