[VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition]

International Conference on Computer Vision (ICCV) 2017

In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available.

Supplementary

• https://www.youtube.com/watch?v=jnewRlt6UbI

Citation

Please cite VPGNet in your publications if it helps your research:

@InProceedings{Lee_2017_ICCV,
  author = {Lee, Seokju and Kim, Junsik and Shin Yoon, Jae and Shin, Seunghak and Bailo, Oleksandr and Kim, Namil and Lee, Tae-Hee and Seok Hong, Hyun and Han, Seung-Hoon and So Kweon, In},
  title = {VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
  month = {Oct},
  year = {2017}
}

Baseline Usage

1. Clone the repository

   git clone https://github.com/SeokjuLee/VPGNet.git

2. Prepare dataset from Caltech Lanes Dataset.
   (Our dataset is currently being reviewed by Samsung Research. This baseline doesn't need VP annotations.)

   • Download Caltech Lanes Dataset.
   • New http://www.vision.caltech.edu/malaa/datasets/caltech-lanes/
   • Organize the file structure as below.

   |__ VPGNet
       |__ caffe
       |__ caltech-lanes-dataset
           |__ caltech-lane-detection/matlab
           |__ cordova1
           |__ cordova2
           |__ washington1
           |__ washington2
           |__ vpg_annot_v1.m

   • Generate list files using 'caltech-lanes-dataset/vpg_annot_v1.m'. Arrange training and validation sets as you wish.

3. Caffe compliation

   • Compile our Caffe codes following the instructions.
   • Move to 'caffe/models/vpgnet-novp'. This is our workspace.

4. Make LMDB

   • Change paths in 'make_lmdb.sh' and run it. The LMDB files would be created.

5. Training

   • Run 'train.sh'

Dataset Contact

• All rights about the dataset are preserved by Samsung Electronics Co.
• Please contact Tae-Hee Lee, Hyun Seok Hong, and Seung-Hoon Han with questions and comments.

Log

• Sep.11.2017: The "VPGNet" pages beta test
• Dec.18.2017: Caffe codes uploaded