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[bibtex]@InProceedings{Gonzalez_2023_ICCV, author = {Gonz\'alez, Cristina and Ayobi, Nicol\'as and Escall\'on, Felipe and Baldovino-Chiquillo, Laura and Wilches-Mogoll\'on, Maria and Pasos, Donny and Ram{\'\i}rez, Nicole and Pinz\'on, Jose and Sarmiento, Olga and Quistberg, D Alex and Arbel\'aez, Pablo}, title = {STRIDE: Street View-Based Environmental Feature Detection and Pedestrian Collision Prediction}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2023}, pages = {3230-3242} }
STRIDE: Street View-Based Environmental Feature Detection and Pedestrian Collision Prediction
Abstract
This paper introduces a novel benchmark to study the impact and relationship of built environment elements on pedestrian collisions prediction, intending to enhance environmental awareness in autonomous driving systems to prevent pedestrian injuries actively. We introduce a built environment detection task in large-scale panoramic images and a detection-based pedestrian collision frequency prediction task. We propose a baseline model that incorporates a collision prediction module into a state-of-the-art detection model to tackle both tasks simultaneously. Our experiments demonstrate a significant correlation between object detection of built environment elements and pedestrian collision frequency prediction. Our results are a stepping stone towards understanding the interdependencies between built environment conditions and pedestrian safety.
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