Database Configuration

##Overview

Single-image, multi-class classification problem More than 40 classes More than 50,000 images in total Large, lifelike database Reliable ground-truth data due to semi-automatic annotation Physical traffic sign instances are unique within the dataset (i.e., each real-world traffic sign only occurs once)

##Structure

The training set archive is structures as follows: One directory per class Each directory contains one CSV file with annotations ("GT-.csv") and the training images Training images are grouped by tracks Each track contains 30 images of one single physical traffic sign

##Image format

The images contain one traffic sign each Images contain a border of 10 % around the actual traffic sign (at least 5 pixels) to allow for edge-based approaches Images are stored in PPM format (Portable Pixmap, P6) Image sizes vary between 15x15 to 250x250 pixels Images are not necessarily squared The actual traffic sign is not necessarily centered within the image.This is true for images that were close to the image border in the full camera image The bounding box of the traffic sign is part of the annotatinos (see below)

##Annotation format

Annotations are provided in CSV files. Fields are separated by ";" (semicolon). Annotations contain the following information: Filename: Filename of corresponding image Width: Width of the image Height: Height of the image ROI.x1: X-coordinate of top-left corner of traffic sign bounding box ROI.y1: Y-coordinate of top-left corner of traffic sign bounding box ROI.x2: X-coordinate of bottom-right corner of traffic sign bounding box ROI.y2: Y-coordinate of bottom-right corner of traffic sign bounding box

The training data annotations will additionally contain ClassId: Assigned class labelId: Assigned class label

##Downloads

Training dataset: Training Data

Test dataset: Test Data