Yet Another Computer Vision Index To Datasets (YACVID) - Details

Stand: 2018-09-19 000000m 08:47:08 - Overview

ok73
Attribute Current Content New
Name (Institute + Shorttitle)wilddash robust 
Description (include details on usage, files and paper references)The WildDash Benchmark provides a dataset and benchmark for semantic and instance segmentation. We aim to improve the expressiveness of performance evaluation for computer vision algorithms in regard to their robustness for driving scenarios under real-world conditions.

Diverse and Challenging Scene Content
We include images from a variety of data sources from all over the world with many different difficult scenarios (e.g. rain, road coverage, darkness, overexposure) and camera characteristics (noise, compression artifacts, distortion). The supplied ground truth format is compatible with Cityscapes.

diverse traffic scenarios
city, highway, and rural locations
scenes from all over the world
poor weather conditions

Focus on Robustness and Performance Evaluation
The main focus of this dataset is testing. It contains data recorded under real world driving situations. Aims of it are:

to compile and provide standard data which can be used for evaluation.
to establish accepted evaluation protocols, data and measures.
to boost the algorithm development on driving applications using computer vision techniques.

for example used in
https://arxiv.org/pdf/1806.03465.pdf 
URL Linkhttp://www.wilddash.cc/ 
Files (#)226 
References (SKIPPED)
Category (SKIPPED) 
Tags (single words, spaced)robust segmentation noise environment lighting fog semantic autonomous 
Last Changed2018-09-19 
Turing (2.12+3.25=?) :-)