Action Classification

UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240. Source: Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification Image Source: https://www.crcv.ucf.edu/data/UCF101.php

The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance. Source: Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors Image Source: https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database

The ActivityNet dataset contains 200 different types of activities and a total of 849 hours of videos collected from YouTube. ActivityNet is the largest benchmark for temporal activity detection to date in terms of both the number of activity categories and number of videos, making the task particularly challenging. Version 1.3 of the dataset contains 19994 untrimmed videos in total and is divided into three disjoint subsets, training, validation, and testing by a ratio of 2:1:1. On average, each activity category has 137 untrimmed videos. Each video on average has 1.41 activities which are annotated with temporal boundaries. The ground-truth annotations of test videos are not public. Source: Dynamic Temporal Pyramid Network: A Closer Look at Multi-Scale Modeling for Activity Detection

The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. Source: https://arxiv.org/abs/1705.06950 Image source: https://arxiv.org/abs/1705.06950

The Charades dataset is composed of 9,848 videos of daily indoors activities with an average length of 30 seconds, involving interactions with 46 objects classes in 15 types of indoor scenes and containing a vocabulary of 30 verbs leading to 157 action classes. Each video in this dataset is annotated by multiple free-text descriptions, action labels, action intervals and classes of interacting objects. 267 different users were presented with a sentence, which includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence. In total, the dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos. In the standard split there are7,986 training video and 1,863 validation video. Source: Temporal Reasoning Graph for Activity Recognition

The THUMOS14 (THUMOS 2014) dataset is a large-scale video dataset that includes 1,010 videos for validation and 1,574 videos for testing from 20 classes. Among all the videos, there are 220 and 212 videos with temporal annotations in validation and testing set, respectively. Source: Learning to Localize Actions from Moments Image Source: http://crcv.ucf.edu/THUMOS14/

The 20BN-SOMETHING-SOMETHING V2 dataset is a large collection of labeled video clips that show humans performing pre-defined basic actions with everyday objects. The dataset was created by a large number of crowd workers. It allows machine learning models to develop fine-grained understanding of basic actions that occur in the physical world. It contains 220,847 videos, with 168,913 in the training set, 24,777 in the validation set and 27,157 in the test set. There are 174 labels. Source Image Source

YouCook2 is the largest task-oriented, instructional video dataset in the vision community. It contains 2000 long untrimmed videos from 89 cooking recipes; on average, each distinct recipe has 22 videos. The procedure steps for each video are annotated with temporal boundaries and described by imperative English sentences (see the example below). The videos were downloaded from YouTube and are all in the third-person viewpoint. All the videos are unconstrained and can be performed by individual persons at their houses with unfixed cameras. YouCook2 contains rich recipe types and various cooking styles from all over the world. Source: http://youcook2.eecs.umich.edu/ Image Source: https://competitions.codalab.org/competitions/20594

The Kinetics-600 is a large-scale action recognition dataset which consists of around 480K videos from 600 action categories. The 480K videos are divided into 390K, 30K, 60K for training, validation and test sets, respectively. Each video in the dataset is a 10-second clip of action moment annotated from raw YouTube video. It is an extensions of the Kinetics-400 dataset. Source: Learning to Localize Actions from Moments Image Source: https://towardsdatascience.com/downloading-the-kinetics-dataset-for-human-action-recognition-in-deep-learning-500c3d50f776

Kinetics-700 is a video dataset of 650,000 clips that covers 700 human action classes. The videos include human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging. Each action class has at least 700 video clips. Each clip is annotated with an action class and lasts approximately 10 seconds.

BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of action labels for about 43 hours of mocap sequences from AMASS. Action labels are at two levels of abstraction -- sequence labels describe the overall action in the sequence, and frame labels describe all actions in every frame of the sequence. Each frame label is precisely aligned with the duration of the corresponding action in the mocap sequence, and multiple actions can overlap. There are over 28k sequence labels, and 63k frame labels in BABEL, which belong to over 250 unique action categories. Labels from BABEL can be leveraged for tasks like action recognition, temporal action localization, motion synthesis, etc.

WLASL is a large video dataset for Word-Level American Sign Language (ASL) recognition, which features 2,000 common different words in ASL. Source: https://github.com/dxli94/WLASL

A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Source: SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

A benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). Source: SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos

CelebV-HQ is a large-scale video facial attributes dataset with annotations. CelebV-HQ contains 35,666 video clips involving 15,653 identities and 83 manually labeled facial attributes covering appearance, action, and emotion. GitHub repository: https://github.com/celebv-hq/celebv-hq

A large scale dataset with daily-living activities performed in a natural manner. Source: Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection

Toyota Smarthome Trimmed has been designed for the activity classification task of 31 activities. The videos were clipped per activity, resulting in a total of 16,115 short RGB+D video samples. activities were performed in a natural manner. As a result, the dataset poses a unique combination of challenges: high intra-class variation, high-class imbalance, and activities with similar motion and high duration variance. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome Trimmed from other datasets for activity classification.

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

A database with 2,000 videos captured by surveillance cameras in real-world scenes. Source: RWF-2000: An Open Large Scale Video Database for Violence Detection

HAA500 is a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591k labeled frames. Unlike existing atomic action datasets, where coarse-grained atomic actions were labeled with action-verbs, e.g., "Throw", HAA500 contains fine-grained atomic actions where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball", to minimize ambiguities in action classification. HAA500 has been carefully curated to capture the movement of human figures with less spatio-temporal label noises to greatly enhance the training of deep neural networks. Source: HAA500: Human-Centric Atomic Action Dataset with Curated Videos

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Is a collection of action videos from many different countries. The motivation is to create a public dataset that would benefit training and pretraining of action recognition models for everybody, rather than making it useful for limited countries. Source: AViD Dataset: Anonymized Videos from Diverse Countries

We describe the 2020 edition of the DeepMind Kinetics human action dataset, which replenishes and extends the Kinetics-700 dataset. In this new version, there are at least 700 video clips from different YouTube videos for each of the 700 classes. This paper details the changes introduced for this new release of the dataset and includes a comprehensive set of statistics as well as baseline results using the I3D network.

• The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition. * Goal : Exploring the concept of constructing training examples for Activities of Daily Living (ADL) recognition by playing life simulation video games. * ** Sims4Action dataset is created with the commercial game THE SIMS 4** by executing actions-of-interest within the game in a "top-down" manner. It features ten hours of video material of eight diverse characters and multiple environments. Ten actions are selected to have a direct correspondence to categories covered in the real-life dataset Toyota Smarthome [2] to enable the research of Synthetic→Real transfer in action recognition. * Two benchmarks :* Gaming→Gaming* (training and evaluation on Sims4Action) and Gaming→Real (training on Sims4Action, evaluation on the real Toyota Smarthome data [2]). * Main challenge: Gaming→Real domain adaptation While ADL recognition on gaming data is interesting from a theoretical perspective, the key challenge arises from transferring knowledge learned from simulated data to real-world applications. Sims4Action specifically provides a benchmark for this scenario since it describes a Gaming→Real challenge, which evaluates models on real videos derived from the existing Toyota Smarthome dataset . # References [1] Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games. Alina Roitberg*, David Schneider*, Aulia Djamal, Constantin Seibold, Simon Reiß, Rainer Stiefelhagen, In International Conference on Intelligent Robots and Systems (IROS), 2021 (* denotes equal contribution.) [2] Toyota smarthome: Real-world activities of daily living. Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond, Gianpiero Francesca, In International Conference on Computer Vision (ICCV), 2019.

Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks. Source: SoccerDB: A Large-Scale Database for Comprehensive Video Understanding

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.

This task offers researchers an opportunity to test their fine-grained classification methods for detecting and recognizing strokes in table tennis videos. (The low inter-class variability makes the task more difficult than with usual general datasets like UCF-101.) The task offers two subtasks: Subtask 1: Stroke Detection: Participants are required to build a system that detects whether a stroke has been performed, whatever its class, and to extract its temporal boundaries. The aim is to be able to distinguish between moments of interest in a game (players performing strokes) from irrelevant moments (between strokes, picking up the ball, having a break…). This subtask can be a preliminary step for later recognizing a stroke that has been performed. Subtask 2: Stroke Classification: Participants are required to build a classification system that automatically labels video segments according to a performed stroke. There are 20 possible stroke classes. Compared with Sports Video 2020, this year we extend the task in the direction of detection and also enrich the dataset with new and more diverse stroke samples. The overview paper of the task is already available here. Participants are encouraged to make their code public with their submission. We provide a public baseline, have a look here.

TTStroke-21 for MediaEval 2022. The task is of interest to researchers in the areas of machine learning (classification), visual content analysis, computer vision and sport performance. We explicitly encourage researchers focusing specifically in domains of computer-aided analysis of sport performance. Our focus is on recordings that have been made by widespread and cheap video cameras, e.g. GoPro. We use a dataset specifically recorded at a sport faculty facility and continuously completed by students and teachers. This dataset is constituted of player-centered videos recorded in natural conditions without markers or sensors. It comprises 20 table strokes, and a rejection class. The problem is hence a typical research topic in the field of video indexing: for a given recording, we need to label the video by recognizing each stroke appearing in it. Ground truth The annotations consist in a description of the handedness of the player and information for each stroke performed (starting and ending frames, class of the stroke). The annotation process was designed as a crowdsourcing method. The annotation sessions are supervised by professional table tennis players and teachers, where the annotator spots and labels strokes in videos using a user-friendly web platform developed. We had a team of 15 annotators, professionals in the field of table tennis. Since a video can be annotated by several annotators, stroke detection according to the annotations was necessary. Our dataset is player-centered, with only one player in each video. An overlap between each annotation of 25% of the annotated stroke duration is allowed. Indeed, during matches with fast exchanges, the boundaries between strokes are hard to determine and annotators would sometimes overlap the annotations between two successive strokes. Evaluation methodology Twenty stroke classes and a non-stroke class are considered according to the rules of table tennis. This taxonomy was designed with professional table tennis teachers. We are working on videos recorded at the Faculty of Sports of the University of Bordeaux. Students are the sportsmen filmed and the teachers are supervising exercises conducted during the recording sessions. The recordings are markerless and allow the players to perform in natural conditions. Subtask 1: for the classification subtask the table tennis videos are trimmed. The trimmed videos are distributed across the considered classes in the train and validation sets. A test set is provided without the distribution information. The participants are asked to fill an xml file with the prediction of their classification model. Submissions will be evaluated in terms of accuracy per class and global accuracy. Subtask 2: for the detection subtask, supplementary videos are provided untrimmed and distributed across train, validation and test sets. For the train and validation sets, the temporal boundaries of the performed strokes are supplied in an xml file. The participants are asked to fill the empty xml files dedicated to the test video with the stroke boundaries inferred by their method. The IoU metric on temporal segments will be used for evaluation.

WiFall dataset contains data related to fall detection, action recognition and people id identification in a meeting room scenario. The dataset provides synchronised CSI, RSSI, and timestamp for each sample.

This dataset provides high-resolution videos recorded from three perspectives with more than 110 hours of total playtime showing mice solving complex tasks. We provide frame-level action labels that reflect a mouse's actions (in proximity to, touch, bite, lock, unlock, touch reward) with lockbox mechanisms (lever, stick, ball, sliding door) for 13% of the data.

First of its kind paired win-fail action understanding dataset with samples from the following domains: “General Stunts,” “Internet Wins-Fails,” “Trick Shots,” & “Party Games.” The task is to identify successful and failed attempts at various activities. Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible.

InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts. The dataset features: + 100 videos per exercise, spanning 5 to 10 repetitions each (1,000 videos total) + 7 unique indoor scenes + Realistic environmental occlusion (+ corresponding labels) + Diverse lighting conditions + Varied body shape, skin tones, and clothing + Rich annotations for 2D and 3D supervision ## Exercises The dataset currently includes the following exercises: + Pushups + Alternating Bicep Curls (with dumbbells) + Delt Flys (with dumbbells) + Squats + Bird Dogs + Supermans + Bicycle Crunches + Leg Raises + Front Raises (with dumbbells) + Overhead Press (with dumbbells) ## Annotations The dataset includes the following annotations: + Bounding boxes + Segmentation masks + Keypoints + Joint angles (quaternions) + Percent occlusion + Avatar characteristics + Camera position + and more Want depth labels? They are not included in the dataset but we can send them to you. Email us at info@toinfinity.ai. ## Download Download the dataset: toinfinity.ai/infiniterep Github repo with additional documentation: https://github.com/toinfinityai/InfiniteRep ## Need more data? Infinity AI specializes in generating custom synthetic data. If you need more (or different data), drop us a line at info@toinfinity.ai (we read every email).