PedAnalyze - Pedestrian Behavior Annotator and Ontology

Taorui Huang, Golam Md Muktadir, Srishti Sripada, Rishi Saravanan, Amelia Yuan, Jim Whitehead

Abstract: Developing safer autonomous vehicles necessitates extensive testing of pedestrian behavior, particularly in atypical situations. Existing datasets lack consistent annotations, with text-based explanations and per-frame annotations causing redundancy and obscuring temporal relationships. To address these issues, we propose PedAnalyze, a Python-based annotator that focuses on pedestrian and vehicle behavior and facilitates structured datasets with pre-defined tags. Our approach allows for both single-frame and multi-frame annotations, which reduces the number of repetitive tasks. In addition, we focus on curating datasets from dash-cam videos on platforms such as YouTube, capturing valuable and rare pedestrian-vehicle incidents. We aim to create a comprehensive pedestrian behavior ontology and dataset to advance autonomous driving system research and development.

External IDs:dblp:conf/ivs/HuangMSSYW24