Towards Predicting Pedestrian Paths: Identifying Surroundings from Monocular Video.

Pedestrian behavior is an essential subject of study when developing or enhancing urban infrastructure. However, most behavior elicitation techniques are inherently bound to be biased by either the observer, the subject, or the environment. The SIMUSAFE project aims at collecting road users’ behavioral data in naturalistic and realistic scenarios to produce more accurate decision-making models. Using video captured from a monocular camera worn by a pedestrian, we employ machine learning and computer vision techniques to identify areas of interest surrounding a pedestrian. Namely, we use object detection and depth estimation to generate a map of obstacles that may influence the pedestrian’s actions. Our methods have shown to be successful in detecting free and occupied areas from monocular video.