Density-Guided Label Smoothing for Temporal Localization of Driving Actions

arXiv:2403.06616v1 Announce Type: new
Abstract: 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 …

abstract advanced arxiv cs.cv driver driving focus however localization performance reliability requirements robustness role studies systems temporal type work