CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

Citation Author(s):
Dogancan
Temel
Georgia Institute of Technology
Gukyeong
Kwon
Georgia Institute of Technology
Mohit
Prabhushankar
Georgia Institute of Technology
Ghassan
AlRegib
Georgia Institute of Technology
Submitted by:
Dogancan Temel
Last updated:
Sun, 10/13/2019 - 17:08
DOI:
10.21227/n4xw-cg56
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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 (>2M images) traffic sign recognition dataset (CURE-TSR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the CURE-TSR dataset were cropped from the CURE-TSD dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Real-world images were obtained from the BelgiumTS video sequences and simulated images were generated with the Unreal Engine 4 game development tool.  Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. Unreal and real sequences were processed with state-of-the-art visual effect software Adobe(c) After Effects to simulate challenging conditions, which include rain, snow, haze, shadow, darkness, brightness, blurriness, dirtiness, colorlessness, sensor and codec errors. Please refer to our GitHub page for code, papers, and more information.

Instructions: 

The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp"

  • sequenceType: 01 - Real data 02 - Unreal data

  • signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking

  • 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.

  • Index: A number shows different instances of traffic signs in the same conditions.

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[1] Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib, "CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/n4xw-cg56. Accessed: Dec. 06, 2019.
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doi = {10.21227/n4xw-cg56},
url = {http://dx.doi.org/10.21227/n4xw-cg56},
author = {Dogancan Temel; Gukyeong Kwon; Mohit Prabhushankar; Ghassan AlRegib },
publisher = {IEEE Dataport},
title = {CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition},
year = {2019} }
TY - DATA
T1 - CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
AU - Dogancan Temel; Gukyeong Kwon; Mohit Prabhushankar; Ghassan AlRegib
PY - 2019
PB - IEEE Dataport
UR - 10.21227/n4xw-cg56
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Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib. (2019). CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition. IEEE Dataport. http://dx.doi.org/10.21227/n4xw-cg56
Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib, 2019. CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition. Available at: http://dx.doi.org/10.21227/n4xw-cg56.
Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib. (2019). "CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition." Web.
1. Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib. CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/n4xw-cg56
Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib. "CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition." doi: 10.21227/n4xw-cg56