Cross-Weather Traffic Scene Understanding Benchmark


Understanding traffic scene images taken from vehicle mounted cameras provides important information for high level tasks such as autonomous driving and advanced driver assistance. The problem is far from trivial especially due to challenges from weather and illumination variation. To facilitate the research against such challenges, we present a new benchmark for cross-weather traffic scene understanding. The dataset consists of 1,356 traffic scene images collected at 226 different locations. For each location, there are six images taken by a vehicle mounted camera under different weather/illumination conditions including sunny day, night, snowy day, rainy night, cloudy day I and cloudy day II. We manually annotated each image with scene understanding labels such as road, sky, building, etc. To the best of our knowledge, this is the first carefully collected benchmark for cross-weather traffic scenes.



A Benchmark for Cross-Weather Traffic Scene Understanding
Shuai Di, Honggang Zhang, Xue Mei, Danil Prokhorov, and Haibin Ling
International Conference on Intelligent Transportation Systems (ITSC), 2016.


How to download the benchmark?

All the data including original images and human annotation can be downloaded entirely

How to use the data?

Txt file listing classes and label colors as RGB triples.

The data in contain the following folders:



The Foggy-Sunny data.



This work is supported by NSF Grant No. 1449860.


If you have any questions, please contact Shuai Di at renjie130 AT