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8 results for “Sign Recognition”
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>
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.
Examining the Efficacy of the Anxiety Meter for Recognition and Management of Anxiety Signs in Autism Spectrum Disorder
ClinicalTrials.gov study NCT02160691. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Novel Visual Education Tool to Improve Recognition and Reporting of Postpartum Urgent Maternal Warning Signs
ClinicalTrials.gov study NCT06912776. IPD Sharing: NO. Countries: 1. Publications: 3.
INCLUDE: A Large Scale Dataset for Indian Sign Language Recognition
<p><strong>Dataset Details</strong>: The INCLUDE dataset has 4292 videos (the paper mentions 4287 videos but 5 videos were added later). The videos used for training are mentioned in train.csv (3475), while that used for testing is mentioned in test.csv (817 files). Each video is a recording of 1 ISL sign, signed by deaf students from St. Louis School for the Deaf, Adyar, Chennai.</p> <p>INCLUDE50 has 766 train videos and 192 test videos.</p> <p><strong>Train-Test Split:</strong> Please download the train-test split for INCLUDE and INCLUDE50 from here: <a href="https://drive.google.com/file/d/1tGjOR9xbk279ZUYnmk3BE-ktcWBdS24w/view?usp=sharing">Train-Test Split</a></p> <p><strong>Publication Link</strong><em>: <a href="https://dl.acm.org/doi/10.1145/3394171.3413528">https://dl.acm.org/doi/10.1145/3394171.3413528</a></em></p> <p><strong>AI4Bharat website</strong><em>: <a href="https://ai4bharat.org/include-dataset">https://sign-language.ai4bharat.org/</a></em></p> <p><strong>Download Instructions</strong></p> <p>For ease of access, we have prepared a Shell Script to download all the parts of the dataset and extract them to form the complete INCLUDE dataset.</p> <p><em>You can find the script here: <a href="http://bit.ly/include_dl">http://bit.ly/include_dl</a></em></p> <p><strong>Paper Abstract:</strong> <em>Indian Sign Language (ISL) is a complete language with its own grammar, syntax, vocabulary and several unique linguistic attributes. It is used by over 5 million deaf people in India. Currently, there is no publicly available dataset on ISL to evaluate Sign Language Recognition (SLR) approaches. In this work, we present the Indian Lexicon Sign Language Dataset - INCLUDE - an ISL dataset that contains 0.27 million frames across 4,287 videos over 263 word signs from 15 different word categories. INCLUDE is recorded with the help of experienced signers to provide close resemblance to natural conditions. A subset of 50 word signs is chosen across word categories to define INCLUDE-50 for rapid evaluation of SLR methods with hyperparameter tuning. The best performing model achieves an accuracy of 94.5% on the INCLUDE-50 dataset and 85.6% on the INCLUDE dataset</em></p>
Recognition of Second Language Spoken Words, Signs, and Characters Via Perception and Production in Adults
ClinicalTrials.gov study NCT05587218. IPD Sharing: YES. Countries: 1. Publications: 0.
Traffic Signs Recognition (TSR)
<p>German Traffic Signs Recognition</p>
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