Gesture Recognition

Aff-Wild is a large-scale in-the-wild dataset for valence-arousal estimation from videos with a variety of head poses, illumination conditions and occlusions. Source: Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond

Comprises 11 hand gesture categories from 29 subjects under 3 illumination conditions. Source: A Low Power, Fully Event-Based Gesture Recognition System

A large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification. Source: 11K Hands: Gender recognition and biometric identification using a large dataset of hand images

The EgoGesture dataset contains 2,081 RGB-D videos, 24,161 gesture samples and 2,953,224 frames from 50 distinct subjects. Source: http://www.nlpr.ia.ac.cn/iva/yfzhang/datasets/egogesture.html Image Source: http://www.nlpr.ia.ac.cn/iva/yfzhang/datasets/egogesture.html

The SHREC dataset contains 14 dynamic gestures performed by 28 participants (all participants are right handed) and captured by the Intel RealSense short range depth camera. Each gesture is performed between 1 and 10 times by each participant in two way: using one finger and the whole hand. Therefore, the dataset is composed by 2800 sequences captured. The depth image, with a resolution of 640x480, and the coordinates of 22 joints (both in the 2D depth image space and in the 3D world space) are saved for each frame of each sequence in the dataset. Source: Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition Image Source: http://tosca.cs.technion.ac.il/book/shrec.html

The Microsoft Research Cambridge-12 Kinect gesture data set consists of sequences of human movements, represented as body-part locations, and the associated gesture to be recognized by the system. The data set includes 594 sequences and 719,359 frames—approximately six hours and 40 minutes—collected from 30 people performing 12 gestures. In total, there are 6,244 gesture instances. The motion files contain tracks of 20 joints estimated using the Kinect Pose Estimation pipeline. The body poses are captured at a sample rate of 30Hz with an accuracy of about two centimeters in joint positions. Source: https://pgram.com/dataset/msrc-12-kinect-gesture-data-set/

The NVGesture dataset focuses on touchless driver controlling. It contains 1532 dynamic gestures fallen into 25 classes. It includes 1050 samples for training and 482 for testing. The videos are recorded with three modalities (RGB, depth, and infrared). Source: Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition Image Source: Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network

A novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis. Source: SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Jester Gesture Recognition dataset includes 148,092 labeled video clips of humans performing basic, pre-defined hand gestures in front of a laptop camera or webcam. It is designed for training machine learning models to recognize human hand gestures like sliding two fingers down, swiping left or right and drumming fingers. Source: Gesture Recognition Dataset: Jester

• An underwater dataset collected from several field trials within the EU FP7 project “Cognitive autonomous diving buddy (CADDY)”, where an Autonomous Underwater Vehicle (AUV) was used to interact with divers and monitor their activities. - Purpose: Studying and boosting object classification, segmentation and human pose estimation tasks, where divers use the CADDIAN gesture-based language. - Data were recorded in different environmental conditions that cause various image distortions unique to underwater scenarios, i.e., low contrast, color distortion, and haze. - Dataset characteristics (gesture-related): - 9191 annotated stereo pairs were gathered for 16 classes (gesture types), i.e., 18,382 total samples. - 7190 true negative stereo pairs (14,380 samples) that contain background scenery and divers without gesturing.

The IPN Hand dataset is a benchmark video dataset with sufficient size, variation, and real-world elements able to train and evaluate deep neural networks for continuous Hand Gesture Recognition (HGR). Source: https://github.com/GibranBenitez/IPN-hand

WiGesture dataset contains data related to gesture recognition and people id identification in a meeting room scenario. The dataset provides synchronised CSI, RSSI, and timestamp for each sample. It can be used for research on WiFi-based human gesture recognition and people id identification.

The TCG dataset is used to evaluate Traffic Control Gesture recognition for autonomous driving. The dataset is based on 3D body skeleton input to perform traffic control gesture classification on every time step. The dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. Source: https://arxiv.org/pdf/2007.16072.pdf Image Source: https://github.com/againerju/tcg_recognition

The VIVA challenge’s dataset is a multimodal dynamic hand gesture dataset specifically designed with difficult settings of cluttered background, volatile illumination, and frequent occlusion for studying natural human activities in real-world driving settings. This dataset was captured using a Microsoft Kinect device, and contains 885 intensity and depth video sequences of 19 different dynamic hand gestures performed by 8 subjects inside a vehicle. Source: Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition Image Source: http://www.site.uottawa.ca/research/viva/projects/hand_detection/index.html

This database contains images of 16 handshapes of the Argentinian Sign Language (LSA), each performed 5 times by 10 different subjects, for a total of 800 images. The subjects wore color hand gloves and dark clothes. Recording The dataset was recorded in an indoors environment, with artificial lightning. Subjects wore dark clothes and performed the handshapes standing, with a white wall as a background. To simplify the problem of hand segmentation, subjects wore fluorescent-colored gloves. These substantially simplify the problem of recognizing the position of the hand and performing its segmentation, and remove all issues associated to skin color variations, while fully retaining the difficulty of recognizing the handshape. The subjects performed the same handshape with both hands. Each handshape was executed imposing few constraints on the subjects to increase diversity and realism in the database. All subjects were non-signers and right-handed, were taught how to perform the handshape during the shooting session by showing them an image of the handshape as performed by one of the authors, and practiced each handshape a few times before recording. We employed a generic webcam for the recording, with a resolution of 640 by 480.

The OREBA dataset aims to provide a comprehensive multi-sensor recording of communal intake occasions for researchers interested in automatic detection of intake gestures. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands.

We manually labelled 3359 images from the RWTH-PHOENIX-Weather 2014 Development set. Some of the 45 encountered pose-independent hand shape classes are depicted in Figure 1. They show the large intra-class variance and the strong similarity between several classes. The hand shapes occur with different frequency in the data. The distribution of counts per class can be verified in Figure 2 showing that the top 14 hand shapes explain 90% of the annotated samples. For our works on hand shape recognition we follow the hand shape taxonomy by the danish sign language lexicon team (Jette H. Kristoffersen and Thomas Troelsgård, Center for Tegnsprog, Denmark http://www.tegnsprog.dk), which amounts to over 60 different hand shapes, often with very subtle differences such as a flexed versus straight thumb. The employed classes are shown in Table1.

Contains static tasks as well as a multitude of more dynamic tasks, involving larger motion of the hands. The dataset has 55 tremor patient recordings together with: associated ground truth accelerometer data from the most affected hand, RGB video data, and aligned depth data. Source: Hand-tremor frequency estimation in videos

The size of the data set is about 1GB. The data set consists of 900 image sequences of 9 gesture classes, which are defined by 3 primitive hand shapes and 3 primitive motions. Therefore, the target task for this data set is to classify different shapes as well as different motions at a time.

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Driver Micro Hand Gestures (DriverMHG) is a dataset for dynamic recognition of driver micro hand gestures, which consists of RGB, depth and infrared modalities.

A large-scale isolated Indian sign language dataset. It contains 2002 common words, used in daily communications among Indian deaf community. The dataset contains 40033 videos across 2002 words. The total duration of the dataset is around 36.2 hours with 7.8 Million frames.

The Florentine dataset is a dataset of facial gestures which contains facial clips from 160 subjects (both male and female), where gestures were artificially generated according to a specific request, or genuinely given due to a shown stimulus. 1032 clips were captured for posed expressions and 1745 clips for induced facial expressions amounting to a total of 2777 video clips. Genuine facial expressions were induced in subjects using visual stimuli, i.e. videos selected randomly from a bank of Youtube videos to generate a specific emotion. Source: Deep video gesture recognition using illumination invariants Image Source: https://www.micc.unifi.it/resources/datasets/florence-3d-faces/

Explicitly created for Human Computer Interaction (HCI). Source: Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons

MlGesture is a dataset for hand gesture recognition tasks, recorded in a car with 5 different sensor types at two different viewpoints. The dataset contains over 1300 hand gesture videos from 24 participants and features 9 different hand gesture symbols. One sensor cluster with five different cameras is mounted in front of the driver in the center of the dashboard. A second sensor cluster is mounted on the ceiling looking straight down. Source: MLGESTURE DATASET

We introduce a large-scale video dataset Slovo for Russian Sign Language task. Slovo dataset size is about 16 GB, and it contains 20400 RGB videos for 1000 sign language gestures from 194 singers. Each class has 20 samples. The dataset is divided into training set and test set by subject user_id. The training set includes 15300 videos, and the test set includes 5100 videos. The total video recording time is ~9.2 hours. About 35% of the videos are recorded in HD format, and 65% of the videos are in FullHD resolution. The average video length with gesture is 50 frames. Annotation file is easy to use and contains some useful columns, see annotations.csv file: | | attachment_id | user_id | width | height | length | text | train | begin | end | |---:|:--------------|:--------|------:|-------:|-------:|-------:|:--------|:------|:----| | 0 | de81cc1c-... | 1b... | 1440 | 1920 | 14 | привет | True | 30 | 45 | | 1 | 3c0cec5a-... | 64... | 1440 | 1920 | 32 | утро | False | 43 | 66 | | 2 | d17ca986-... | cf... | 1920 | 1080 | 44 | улица | False | 12 | 31 | where: - attachment_id - video file name - user_id - unique anonymized user ID - width - video width - height - video height - length - video length - text - gesture class in Russian Langauge - train - train or test boolean flag - begin - start of the gesture (for original dataset) - end - end of the gesture (for original dataset)

The dataset consists of images of Human palms captured using a mobile phone. The images have been taken in a real-world scenario like holding objects or performing simple gestures. The dataset has a wide variety of variations like illumination, distances etc. It consists of images of 3 main gestures: Frontal-open palm, Back open palm and fist with the wrist. It also has a lot of images with people wearing gloves. Dataset Features: - Captured by 4000+ unique users - Covers wide variety of palm images in indoor and outdoor scenes - Images of palm with gloves - Captured by male and female - Distributed age groups like teenage, adults and old - Captured using mobile phones - Highly diverse - Various lighting conditions like day, night, indoor and outdoor - Outdoor scene with a variety of viewpoints Annotations Format: - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us at (sales@datacluster.ai)[sales@datacluster.ai] Visit www.datacluster.ai to know more.