End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation
This work addresses false alarms in pedestrian collision warning systems for drivers, but it is incremental as it builds on existing CNN and semantic segmentation methods.
The paper tackled the problem of false alarms in pedestrian collision warning systems by proposing an end-to-end framework based on a convolutional neural network with semantic segmentation, resulting in reduced false alarms and increased warning accuracy compared to a traditional HoG-based system.
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.