End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation

TL;DR

This paper introduces an end-to-end pedestrian collision warning system using a convolutional neural network enhanced with semantic segmentation, significantly reducing false alarms and improving warning accuracy over traditional methods.

Contribution

The paper presents a novel CNN-based framework that integrates semantic segmentation and dual loss functions for more accurate pedestrian collision warnings.

Findings

Reduces false alarms compared to traditional systems

Increases warning accuracy with CNN and semantic segmentation

Demonstrates effectiveness through experimental results

Abstract

Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms can make it difficult for drivers to concentrate on their driving. In this paper, we propose a novel framework for an end-to-end pedestrian collision warning system based on a convolutional neural network. Semantic segmentation information is used to train the convolutional neural network and two loss functions, such as cross entropy and Euclidean losses, are minimized. Finally, we demonstrate the effectiveness of our method in reducing false alarms and increasing warning accuracy compared to a traditional histogram of oriented gradients (HoG)-based system.