What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
A new survey paper published on arXiv categorizes attention-worthy elements in urban street scenes to enhance road safety through computer vision. The paper proposes a taxonomy that divides critical traffic entities into anomalies and normal but critical elements, encompassing ten categories and twenty subclasses. It analyzes 35 vision-driven tasks and 73 datasets, highlighting their strengths and weaknesses to guide researchers and optimize resource allocation in the field. AI
IMPACT Provides a structured framework for computer vision research in road safety, identifying gaps and guiding future dataset development.