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10 results for “Traffic signs”
Extended Malaysian Traffic Sign Dataset (EMTD)
<p>An extension of the existing Malaysian Traffic Sign Dataset for traffic sign (TS) detection. This contains 66 TS categories, and contains an additional 814 new TS instances than the original dataset. Containing 1,413 images in total. In particular, there has been a great increase in classes that previously had fewer than 75 examples</p>
GTSRB - German Traffic Sign Recognition Benchmark by Real-Time Computer Vision at Ruhr-Universität Bochum
<div> <div>The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. <br>Our benchmark has the following properties: <br>- Single-image, multi-class classification problem <br>- More than 40 classes<br>- More than 50,000 images in total <br>- Large, lifelike database<br><br>Acknowledgements: [INI Benchmark Website][1]<br>[1]: http://benchmark.ini.rub.de/</div> </div>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.</p>
German Traffic Sign Detection Benchmark - 3 different types of backdoor patterns added
<p>Datasets are split in Training and Test datasets</p> <p>GTSRB_backdoor_green_1.zip - a green squared pattern sized 1% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p> <p>GTSRB_backdoor_green_0_5.zip - a green squared pattern sized 0.5% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p> <p>GTSRB_backdoor_black_1.zip - a black squared pattern sized 1% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p>
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, 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 (<a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the <a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a> dataset were cropped from the <a href="https://github.com/olivesgatech/CURE-TSD">CURE-TSD</a> 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 <a href="https://github.com/olivesgatech/CURE-TSR">GitHub page</a> for code, papers, and more information.</p> <p>Instructions: </p> <p>The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp"</p> <ul> <li> <p>sequenceType: 01 - Real data 02 - Unreal data</p> </li> <li> <p>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</p> </li> <li> <p>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</p> </li> <li> <p>challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.</p> </li> <li> <p>Index: A number shows different instances of traffic signs in the same conditions.</p> </li> </ul>
The Bangladesh Road Traffic Sign Dataset in Real-World Images for Traffic Sign Recognition
Open the record for dataset details and reuse information.
German Traffic Sign Detection Benchmark (resized to 128x128)
<p>German Traffic Sign Detection Benchmark Dataset</p> <p>All images resized to 128x128</p> <p>Split in Training and Test datasets according to source</p> <p> </p> <p>Original data available under: http://benchmark.ini.rub.de/?section=gtsrb</p>
Dataset of a project entitled "Knowledge of traffic signs among vehicle drivers of Bangladesh"
<p>Road traffic accidents remain a significant public health concern globally, causing a staggering number of fatalities and injuries annually. Our study delves into the knowledge and perceptions of traffic signs among bus drivers in Bangladesh, a country witnessing a notable surge in economic growth and urbanization, leading to increased motorization and road accidents. The research examines the level of traffic sign knowledge and perceptions among 300 licensed bus drivers using a cross-sectional survey approach. The results provide insights regarding the driver's understanding and shed light on key factors contributing to comprehension levels, including age, education, and training.</p>
Traffic Signs Recognition (TSR)
<p>German Traffic Signs Recognition</p>
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