This repository provides code for parsing the DriveU Traffic Light Dataset (DTLD), which is published in the course of our 2018 ICRA publication "The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets".
Paper see https://ieeexplore.ieee.org/document/8460737.
INFO (11/27/2018): The Dataset is online now!
The data can be downloaded from http://www.traffic-light-data.com/.
.
├── DTLD # DTLD
├── Berlin # Contains all Routes of Berlin
├── Bochum # Contains all routes of Bochum
├── Bremen # Contains all routes of Bremen
├── Dortmund # Contains all routes of Dortmund
├── Duesseldorf # Contains all routes of Duesseldorf
├── Essen # Contains all routes of Essen
├── Frankfurt # Contains all routes of Frankfurt
├── Fulda # Contains all routes of Fulda
├── Hannover # Contains all routes of Hannover
├── Kassel # Contains all routes of Kassel
├── Koeln # Contains all routes of Cologne
├── Berlin_all.yml # Label file for Berlin
├── ...
├── Koeln_all.yml # Label file for Cologne
├── DTLD_all.yml # Complete label file
├── DTLD_train.yml # Training file
├── DTLD_test.yml # Testing file
├── LICENSE # License
└── README.md # Readme
We separated each drive in one city into different routes
.
├── Berlin # Berlin
├── Berlin1 # First route
├── Berlin2 # Second route
├── Berlin3 # Third route
├── ...
We separated each route into several sequences. One sequence describes one unique intersection up to passing it. The foldername indicates date and time.
.
├── Berlin 1 # Route Berlin1
├── 2015-04-17_10-50-05 # First intersection
├── 2015-04-17_10-50-41 # Second intersection
├── ...
For each sequences, images and disparity images are available. Filename indicates time and date
.
├── 2015-04-17_10-50-05 # Route Berlin1
├── DE_BBBR667_2015-04-17_10-50-13-633939_k0.tiff # First left camera image
├── DE_BBBR667_2015-04-17_10-50-13-633939_nativeV2.tiff # First disparity image
├── DE_BBBR667_2015-04-17_10-50-14-299876_k0.tiff # Second left camera image
├── DE_BBBR667_2015-04-17_10-50-14-299876_nativeV2 # Second disparity image
├── ...
Documentation is stored at /dtld_parsing/doc/. We give insights into the data and explain how to interpret it.
Do not forget to change the absolute paths of the images in all label files (.yml).
- Clone the dtld_parsing respository
git clone https://github.com/julimueller/dtld_parsing
- Build everything
1. cd dtld_parsing/C++/driveu_dataset/
2. mkdir build && cd build
3. cmake .. -DCMAKE_INSTALL_PREFIX="YOUR_PATH" && make -j12 install
4. driveu_test -label_file <label_file_path.yml> -calib_dir <path_to_calib> -data_base_dir <dtld_dir>
Note: "YOUR_PATH" has to be in LD_LIBRARY_PATH. DTLD_DIR is the directory where all .zips should be unpacked. The visualization should look like this
git clone https://github.com/julimueller/dtld_parsing
cd dtld_parsing/Python
python load_dtld.py --label_file <label_file_path.yml> --calib_dir <path_to_calib> --data_base_dir <dtld_dir>
Result should look like above
Run main.m
Do not forget to cite our work for the case you used DTLD
@INPROCEEDINGS{8460737,
author={A. Fregin and J. Müller and U. Kreβel and K. Dietmayer},
booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
title={The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets},
year={2018},
volume={},
number={},
pages={3376-3383},
keywords={computer vision;image recognition;traffic engineering computing;DriveU traffic light dataset;traffic light recognition;autonomous driving;computer vision;University of Ulm Traffic Light Dataset;Daimler AG;Cameras;Urban areas;Benchmark testing;Lenses;Training;Visualization;Detectors},
doi={10.1109/ICRA.2018.8460737},
ISSN={2577-087X},
month={May},}