Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks

TL;DR

This paper presents a real-time convolutional neural network system that detects pedestrians to automatically activate crosswalk signals, aiming to enhance street crossing safety especially for vulnerable pedestrians.

Contribution

It introduces a CNN-based system trained on a new dataset to automatically detect pedestrians and trigger crosswalk signals, improving safety measures.

Findings

System can operate in real-time environments

Effective in distinguishing pedestrians from false alarms

Feasible as a backup to existing crosswalk signals

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.