  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.
