Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural
Networks
- URL: http://arxiv.org/abs/2208.07250v1
- Date: Mon, 8 Aug 2022 22:48:22 GMT
- Title: Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural
Networks
- Authors: Eric Liang and Mark Stamp
- Abstract summary: Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties.
People often forget to activate a crosswalk light or are unable to do so.
In this paper, we consider an improvement to the crosswalk system by designing a system that can detect pedestrians.
- Score: 7.227853810310584
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A common yet potentially dangerous task is the act of crossing the street.
Pedestrian accidents contribute a significant amount to the high number of
annual traffic casualties, which is why it is crucial for pedestrians to use
safety measures such as a crosswalk. However, people often forget to activate a
crosswalk light or are unable to do so -- such as those who are visually
impaired or have occupied hands. Other pedestrians are simply careless and find
the crosswalk signals a hassle, which can result in an accident where a car
hits them. In this paper, we consider an improvement to the crosswalk system by
designing a system that can detect pedestrians and triggering the crosswalk
signal automatically. We collect a dataset of images that we then use to train
a convolutional neural network to distinguish between pedestrians (including
bicycle riders) and various false alarms. The resulting system can capture and
evaluate images in real time, and the result can be used to automatically
activate systems a crosswalk light. After extensive testing of our system in
real-world environments, we conclude that it is feasible as a back-up system
that can compliment existing crosswalk buttons, and thereby improve the overall
safety of crossing the street.
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