Abstract
As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the CURE-TSD dataset are grouped into two classes: real data and unreal data. Real data correspond to processed versions of sequences acquired from real world. Unreal data corresponds to synthesized sequences generated in a virtual environment. There are 49 real sequences and 49 unreal sequences that do not include any specific challenge. We separated the sequences into 70% and %30 splits. Therefore, we have 34 training videos and 15 test videos in both real and unreal sequences that are challenge-free. There are 300 frames in each video sequence. There are 49 challenge-free real video sequences processed with 12 different types of effects and 5 different challenge levels, which result in 2,989 (49125+49) video sequences. Moreover, there are 49 synthesized video sequences processed with 11 different types of effects and 5 different challenge levels, which leads to 2,744 (49115+49) video sequences. In total, there are 5,733 video sequences, which include around 1.72 million frames. Please refer to our GitHub page for code, papers, and more information.
Instructions:
The name format of the video files are as follows: “sequenceType_sequenceNumber_challengeSourceType_challengeType_challengeLevel.mp4”
· sequenceType: 01 – Real data 02 – Unreal data
· sequenceNumber: A number in between [01 – 49]
· challengeSourceType: 00 – No challenge source (which means no challenge) 01 – After affect
· challengeType: 00 – No challenge 01 – Decolorization 02 – Lens blur 03 – Codec error 04 – Darkening 05 – Dirty lens 06 – Exposure 07 – Gaussian blur 08 – Noise 09 – Rain 10 – Shadow 11 – Snow 12 – Haze
· challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.
Cite This Work :
@ARTICLE{temel2019traffic,
author={D. Temel and M. Chen and G. AlRegib},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics},
year={2019},
volume={},
number={},
pages={1-11},
doi={10.1109/TITS.2019.2931429},
ISSN={1524-9050},
url={https://arxiv.org/abs/1908.11262}}