Pedestrian Collision Detection through Monocular Vision

Abstract

In this paper, a depth detection based on monocular vision coupled with the motion was used to predict the possible collisions on pedestrian crossings. A video taken while in motion is extracted into frames and processed with skin color detection methods to identify the face blobs of pedestrians. The distance to the pedestrian was determined using the difference in size of the face blobs in the consecutive image frames. The field tests show that the size variation does not depend on the speed of the vehicle but fewer steps are available for processing. The breaking distance which depends on the speed increases with the speed of the vehicle. A trigger for the breaking signal can be fired depending on the face blobs’ size variation. Reasonable results are observed from the field tests, when theoretical and experimental breaking distances are compared.